{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-20T03:48:49.960907Z",
     "start_time": "2025-04-20T03:48:49.776206Z"
    }
   },
   "source": [
    "import json\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:48:49.965470Z",
     "start_time": "2025-04-20T03:48:49.960907Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data_dir = os.path.join(os.getcwd(), \"data\")\n",
    "data_10_dir = os.path.join(data_dir, \"10G_data\")\n",
    "data_30_dir = os.path.join(data_dir, \"30G_data\")"
   ],
   "id": "1849b90e0eb66552",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:48:49.971554Z",
     "start_time": "2025-04-20T03:48:49.965470Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import functools\n",
    "import time\n",
    "def timer(func):\n",
    "    @functools.wraps(func)\n",
    "    def wrapper(*args, **kwargs):\n",
    "        t0 = time.perf_counter()\n",
    "        res = func(*args, **kwargs)\n",
    "        t1 = time.perf_counter()\n",
    "        if t1 - t0 > 1:\n",
    "            print(f\"{func.__name__} took {t1 - t0:.2f}s to execute.\")\n",
    "        else:\n",
    "            print(f\"{func.__name__} took {(t1 - t0) * 1000:.2f}ms to execute.\")\n",
    "        return res\n",
    "\n",
    "    return wrapper"
   ],
   "id": "1b78f4935f8c301c",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:34.273355Z",
     "start_time": "2025-04-20T03:48:49.971554Z"
    }
   },
   "cell_type": "code",
   "source": [
    "@timer\n",
    "def load_data(dir):\n",
    "    total_len, data = 0, []\n",
    "    for file in os.listdir(dir):\n",
    "        if file.endswith('.parquet'):\n",
    "            d = pd.read_parquet(os.path.join(dir, file))\n",
    "            total_len += len(d)\n",
    "            data.append(d.drop_duplicates())\n",
    "    print(f\"Loaded {total_len} rows from {dir}.\")\n",
    "    data = pd.concat(data, ignore_index=True)\n",
    "    data.drop_duplicates(inplace=True)\n",
    "    return data\n",
    "if not os.path.exists(os.path.join(data_dir, \"data_10G.parquet\")):\n",
    "    data = load_data(data_10_dir)\n",
    "    data.to_parquet(os.path.join(data_dir, \"data_10G.parquet\"), index=False)\n",
    "else:\n",
    "    data = pd.read_parquet(os.path.join(data_dir, \"data_10G.parquet\"))\n",
    "print(f\"Remain {len(data)} rows.\")"
   ],
   "id": "72763210c4b3e3cf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 100000000 rows from F:\\研究生学业\\结课作业\\研一下\\数据挖掘\\个人作业\\互评作业1\\code\\data\\10G_data.\n",
      "load_data took 282.89s to execute.\n",
      "Remain 800000 rows.\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:34.359102Z",
     "start_time": "2025-04-20T03:53:34.273355Z"
    }
   },
   "cell_type": "code",
   "source": "data.head()",
   "id": "4ec5246fc6a9c33b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   id                  timestamp   user_name chinese_name  \\\n",
       "0   1  2025-01-09T01:38:20+00:00     UZPFPZJ           彭敏   \n",
       "1   2  2023-07-08T22:53:52+00:00       UEHSG           高杰   \n",
       "2   3  2023-12-31T20:00:57+00:00    fxuujvnk          姜子轩   \n",
       "3   4  2023-03-22T11:12:02+00:00      DDERCI           梁云   \n",
       "4   5  2024-08-10T11:43:08+00:00  RTABMQKQLG           钱俊   \n",
       "\n",
       "                  email  age    income gender country  \\\n",
       "0       xtnlkqsb@qq.com   36   73000.0      女     俄罗斯   \n",
       "1  rkpktrqz@outlook.com   58  223000.0      女      巴西   \n",
       "2  vwnquvla@outlook.com   88  858000.0      男      美国   \n",
       "3       jpajekzz@qq.com   61  485000.0      男      德国   \n",
       "4      haqlhpmb@163.com   33  437000.0      男      中国   \n",
       "\n",
       "           chinese_address                                   purchase_history  \\\n",
       "0  广西壮族自治区绍兴和谐路152号2单元1384  {\"average_price\":15.940000000000001,\"category\"...   \n",
       "1     黑龙江省大连建设路127号1单元1835  {\"average_price\":563.4100000000001,\"category\":...   \n",
       "2       浙江省厦门繁荣路15号5单元1442  {\"average_price\":669.34,\"category\":\"书籍\",\"items...   \n",
       "3       四川省宁波上海路30号6单元2134  {\"average_price\":637.66,\"category\":\"书籍\",\"items...   \n",
       "4       四川省成都康乐路181号6单元391  {\"average_price\":505.0,\"category\":\"家居\",\"items\"...   \n",
       "\n",
       "   is_active registration_date  credit_score  phone_number  \n",
       "0      False        2024-10-02           423  918-668-7857  \n",
       "1      False        2021-03-19           567  205-503-3300  \n",
       "2      False        2022-05-07           767  673-105-7503  \n",
       "3      False        2020-07-08           587  387-482-7104  \n",
       "4      False        2023-10-12           404  602-478-3001  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>user_name</th>\n",
       "      <th>chinese_name</th>\n",
       "      <th>email</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>gender</th>\n",
       "      <th>country</th>\n",
       "      <th>chinese_address</th>\n",
       "      <th>purchase_history</th>\n",
       "      <th>is_active</th>\n",
       "      <th>registration_date</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>phone_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2025-01-09T01:38:20+00:00</td>\n",
       "      <td>UZPFPZJ</td>\n",
       "      <td>彭敏</td>\n",
       "      <td>xtnlkqsb@qq.com</td>\n",
       "      <td>36</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>俄罗斯</td>\n",
       "      <td>广西壮族自治区绍兴和谐路152号2单元1384</td>\n",
       "      <td>{\"average_price\":15.940000000000001,\"category\"...</td>\n",
       "      <td>False</td>\n",
       "      <td>2024-10-02</td>\n",
       "      <td>423</td>\n",
       "      <td>918-668-7857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2023-07-08T22:53:52+00:00</td>\n",
       "      <td>UEHSG</td>\n",
       "      <td>高杰</td>\n",
       "      <td>rkpktrqz@outlook.com</td>\n",
       "      <td>58</td>\n",
       "      <td>223000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>巴西</td>\n",
       "      <td>黑龙江省大连建设路127号1单元1835</td>\n",
       "      <td>{\"average_price\":563.4100000000001,\"category\":...</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-03-19</td>\n",
       "      <td>567</td>\n",
       "      <td>205-503-3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-12-31T20:00:57+00:00</td>\n",
       "      <td>fxuujvnk</td>\n",
       "      <td>姜子轩</td>\n",
       "      <td>vwnquvla@outlook.com</td>\n",
       "      <td>88</td>\n",
       "      <td>858000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>美国</td>\n",
       "      <td>浙江省厦门繁荣路15号5单元1442</td>\n",
       "      <td>{\"average_price\":669.34,\"category\":\"书籍\",\"items...</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-05-07</td>\n",
       "      <td>767</td>\n",
       "      <td>673-105-7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2023-03-22T11:12:02+00:00</td>\n",
       "      <td>DDERCI</td>\n",
       "      <td>梁云</td>\n",
       "      <td>jpajekzz@qq.com</td>\n",
       "      <td>61</td>\n",
       "      <td>485000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>德国</td>\n",
       "      <td>四川省宁波上海路30号6单元2134</td>\n",
       "      <td>{\"average_price\":637.66,\"category\":\"书籍\",\"items...</td>\n",
       "      <td>False</td>\n",
       "      <td>2020-07-08</td>\n",
       "      <td>587</td>\n",
       "      <td>387-482-7104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2024-08-10T11:43:08+00:00</td>\n",
       "      <td>RTABMQKQLG</td>\n",
       "      <td>钱俊</td>\n",
       "      <td>haqlhpmb@163.com</td>\n",
       "      <td>33</td>\n",
       "      <td>437000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>中国</td>\n",
       "      <td>四川省成都康乐路181号6单元391</td>\n",
       "      <td>{\"average_price\":505.0,\"category\":\"家居\",\"items\"...</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-10-12</td>\n",
       "      <td>404</td>\n",
       "      <td>602-478-3001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:36.279265Z",
     "start_time": "2025-04-20T03:53:34.359102Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 删除注册时间晚于记录时间的数据\n",
    "data.timestamp = pd.to_datetime(data.timestamp, utc=True)\n",
    "data.registration_date = pd.to_datetime(data.registration_date, utc=True)\n",
    "invalid_rows = data[data.timestamp < data.registration_date]\n",
    "print(len(invalid_rows))\n",
    "data = data[data.timestamp >= data.registration_date]\n",
    "print(len(data))"
   ],
   "id": "39910d0787ee168b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "404\n",
      "799596\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:36.397168Z",
     "start_time": "2025-04-20T03:53:36.279265Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 删除低价值列\n",
    "data.drop(['id', 'timestamp', 'email', 'country', 'chinese_address',\n",
    "           'is_active', 'phone_number', 'registration_date'], axis=1, inplace=True)\n",
    "data.head()"
   ],
   "id": "7c5675e40dc113ee",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    user_name chinese_name  age    income gender  \\\n",
       "0     UZPFPZJ           彭敏   36   73000.0      女   \n",
       "1       UEHSG           高杰   58  223000.0      女   \n",
       "2    fxuujvnk          姜子轩   88  858000.0      男   \n",
       "3      DDERCI           梁云   61  485000.0      男   \n",
       "4  RTABMQKQLG           钱俊   33  437000.0      男   \n",
       "\n",
       "                                    purchase_history  credit_score  \n",
       "0  {\"average_price\":15.940000000000001,\"category\"...           423  \n",
       "1  {\"average_price\":563.4100000000001,\"category\":...           567  \n",
       "2  {\"average_price\":669.34,\"category\":\"书籍\",\"items...           767  \n",
       "3  {\"average_price\":637.66,\"category\":\"书籍\",\"items...           587  \n",
       "4  {\"average_price\":505.0,\"category\":\"家居\",\"items\"...           404  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>chinese_name</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>gender</th>\n",
       "      <th>purchase_history</th>\n",
       "      <th>credit_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UZPFPZJ</td>\n",
       "      <td>彭敏</td>\n",
       "      <td>36</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>{\"average_price\":15.940000000000001,\"category\"...</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UEHSG</td>\n",
       "      <td>高杰</td>\n",
       "      <td>58</td>\n",
       "      <td>223000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>{\"average_price\":563.4100000000001,\"category\":...</td>\n",
       "      <td>567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fxuujvnk</td>\n",
       "      <td>姜子轩</td>\n",
       "      <td>88</td>\n",
       "      <td>858000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>{\"average_price\":669.34,\"category\":\"书籍\",\"items...</td>\n",
       "      <td>767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DDERCI</td>\n",
       "      <td>梁云</td>\n",
       "      <td>61</td>\n",
       "      <td>485000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>{\"average_price\":637.66,\"category\":\"书籍\",\"items...</td>\n",
       "      <td>587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RTABMQKQLG</td>\n",
       "      <td>钱俊</td>\n",
       "      <td>33</td>\n",
       "      <td>437000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>{\"average_price\":505.0,\"category\":\"家居\",\"items\"...</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:40.917250Z",
     "start_time": "2025-04-20T03:53:36.397168Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 简化购物记录\n",
    "purchase_history = data.purchase_history.apply(\n",
    "    lambda x: ((y := json.loads(x))['average_price'], len(y['items'])))\n",
    "data['price'] = purchase_history.apply(lambda x: x[0]*x[1])\n",
    "data['items'] = purchase_history.apply(lambda x: x[1]).astype(int)\n",
    "data.drop(['purchase_history'], axis=1, inplace=True)\n",
    "data.head()"
   ],
   "id": "d45267d63075cdb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    user_name chinese_name  age    income gender  credit_score    price  items\n",
       "0     UZPFPZJ           彭敏   36   73000.0      女           423   159.40     10\n",
       "1       UEHSG           高杰   58  223000.0      女           567  2253.64      4\n",
       "2    fxuujvnk          姜子轩   88  858000.0      男           767  2677.36      4\n",
       "3      DDERCI           梁云   61  485000.0      男           587  6376.60     10\n",
       "4  RTABMQKQLG           钱俊   33  437000.0      男           404  4545.00      9"
      ],
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       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>chinese_name</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
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       "      <th>0</th>\n",
       "      <td>UZPFPZJ</td>\n",
       "      <td>彭敏</td>\n",
       "      <td>36</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>423</td>\n",
       "      <td>159.40</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UEHSG</td>\n",
       "      <td>高杰</td>\n",
       "      <td>58</td>\n",
       "      <td>223000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>567</td>\n",
       "      <td>2253.64</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fxuujvnk</td>\n",
       "      <td>姜子轩</td>\n",
       "      <td>88</td>\n",
       "      <td>858000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>767</td>\n",
       "      <td>2677.36</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DDERCI</td>\n",
       "      <td>梁云</td>\n",
       "      <td>61</td>\n",
       "      <td>485000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>587</td>\n",
       "      <td>6376.60</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RTABMQKQLG</td>\n",
       "      <td>钱俊</td>\n",
       "      <td>33</td>\n",
       "      <td>437000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>404</td>\n",
       "      <td>4545.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:40.925273Z",
     "start_time": "2025-04-20T03:53:40.917250Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def set_outlier_anomaly(df, columns=None, method='IQR', set_value=np.nan, set_tag=False):\n",
    "    \"\"\"\n",
    "        找到指定数据列中的离群点并设置为空值。\n",
    "        set_tag = True时，增加anomaly列将异常值标记为3。\n",
    "    \"\"\"\n",
    "    # 如果没有指定列，默认为所有数值列\n",
    "    if columns is None:\n",
    "        columns = df.select_dtypes(include=[np.number]).columns\n",
    "    columns = [col for col in columns if col not in [\"alarm_code\", \"max_alarm_lvl\", \"anomaly\"]]\n",
    "\n",
    "    for column in columns:\n",
    "        if method == '3-sigma':\n",
    "            # 使用3-sigma方法判断异常值\n",
    "            mean = df[column].mean()\n",
    "            std = df[column].std()\n",
    "            is_outlier = (df[column] - mean).abs() > 3 * std\n",
    "\n",
    "        elif method == 'IQR':\n",
    "            # 使用IQR方法判断异常值\n",
    "            Q1 = df[column].quantile(0.25)\n",
    "            Q3 = df[column].quantile(0.75)\n",
    "            IQR = Q3 - Q1\n",
    "            is_outlier = (df[column] < Q1 - 1.5 * IQR) | (df[column] > Q3 + 1.5 * IQR)\n",
    "\n",
    "        else:\n",
    "            raise ValueError(\"Unsupported method. Use '3-sigma' or 'IQR'.\")\n",
    "\n",
    "        if set_value is not None:\n",
    "            df.loc[is_outlier, column] = set_value\n",
    "        if set_tag:\n",
    "            if 'anomaly' not in df.columns:\n",
    "                df['anomaly'] = 0\n",
    "            df.loc[is_outlier, 'anomaly'] = 3\n",
    "\n",
    "    return df\n",
    "# data = set_outlier_anomaly(data, set_value=None, set_tag=True)\n",
    "# data[data['anomaly']==3]"
   ],
   "id": "ed971d87ff1808bd",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:42.403963Z",
     "start_time": "2025-04-20T03:53:40.925273Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 设置支持中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False     # 正常显示负号\n",
    "\n",
    "# 年龄分布直方图\n",
    "sns.histplot(data['age'], bins=83)\n",
    "plt.title(\"年龄分布\")\n",
    "plt.show()\n",
    "\n",
    "# 收入分布直方图\n",
    "sns.histplot(data['income'])\n",
    "plt.title(\"收入分布\")\n",
    "plt.show()"
   ],
   "id": "87b3b9c15011bd50",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:42.507841Z",
     "start_time": "2025-04-20T03:53:42.403963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 性别分布饼图\n",
    "gender_counts = data['gender'].value_counts()\n",
    "labels = gender_counts.index.astype(str)\n",
    "sizes = gender_counts.values\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('性别分布')\n",
    "plt.show()"
   ],
   "id": "5db3a370242e0f25",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:43.262783Z",
     "start_time": "2025-04-20T03:53:42.507841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 消费分布直方图   \n",
    "sns.histplot(data['price'])\n",
    "plt.title(\"消费分布\")\n",
    "plt.show()"
   ],
   "id": "8d20194fd46785e2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:43.448488Z",
     "start_time": "2025-04-20T03:53:43.262783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 年龄段 vs 平均消费（分箱 + 分组）\n",
    "# 年龄分段\n",
    "data['age_bin'] = pd.cut(data['age'], bins=[15, 25, 35, 45, 55, 65, 100])\n",
    "\n",
    "# 分组后平均价格\n",
    "age_price = data.groupby('age_bin')['price'].mean().reset_index()\n",
    "\n",
    "sns.barplot(x='age_bin', y='price', data=age_price)\n",
    "plt.xticks(rotation=30)\n",
    "plt.title(\"不同年龄段的平均消费\")\n",
    "plt.show()\n",
    "data.drop(['age_bin'], axis=1, inplace=True)"
   ],
   "id": "875a034ec4bb4bea",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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Lly5h27ZtT23zk27fvo1Dhw6JZ1bdvHkTM2bMQHp6OjZv3owGDRqUGHYuXbqE2NhY3Lx5Uww7lpaWaN68ebHXePjwoc5jFxcXtGzZErNmzcKaNWvQrFkz8awybRDs2LEjZDIZfvjhB3z33Xf49ttvy7VdRFURww5RFfbuu++W6/m2trbo379/ifO0g5qfvHBead0TpqamOHv2rHidnCfDgXbMTtHxHQDEs6W0QUEQBGzbtg3169fHe++9h3PnziEzMxMNGzZEXl4e/Pz8EBwcDAsLC5w4cQIHDhzQeZ1Hjx4hLS0NSqUSCxYswB9//IGrV6/im2++QdeuXTF06FCsW7cOsbGx+O9//4tx48Y9s8utJB4eHoiKioKTkxMePHgAAMW6DJ+HIAhISEhAXFwcMjMz0bNnT7Rp0wbvvPMONm3ahOXLl6NNmzaIiooSQ1lJ44YOHTqEJk2aoGPHjuK0snZj2djYYN68eQBQ7F5ky5Ytg0KhwNatW8VppQVhouqGYYeoGqrITTPt7Ox0unG0ntY9VZEve+2FDLUEQcCgQYPEM4xWrVqFhQsXQhAEmJqaYt68eeL1ZIKCgsSL82lNmjQJv//+O4yNjXH//n0MHDgQM2bMQLt27QAUDkYePHgwPvjgAzg6Oj61y+j27dviv5+8yrFMJhPPWNPeG0qfYcff3x/x8fGoXbs2Ro4ciSFDhsDNzQ1ZWVnYsGED+vbti/nz5xe7rk7RsKNWq7Fr1y4MHDhQZ92lXSX70qVL4mD2J+Xm5or/jo6OxsWLF7F27Vps374dDx8+RKNGjeDr6/u8m01UJTDsEFVDKpWq3L+65XK5TjdOSdRq9VPH8BSlDVzPunlnfn4+fvnlF5w9exZpaWlIS0vD/fv3MX78eACF3Sbff/89xowZU+IZP9OnT8fvv/+Obdu24auvvio2PyEhATdv3oRGo4GxsTH+/PPPYte/0bbxyS63okdHikpJSYGJiUmpY6SKUqvVZbqB6dChQ+Hv7w9/f3+dgdy1atXCf//7X50LIwKFYSQ9PV3nuTExMUhLS9MZgK5Wq2FpaVniGWB//fVXqe3ZsGEDEhMTMXv2bERGRsLf3x/dunXDt99+i4iICDx8+BB9+vTBn3/+KQZVouqKYYeoGlKpVGW6qm3RX+9Ps2/fPuzZswexsbEYMGBAmZbJz88H8OyjTAEBASgoKED9+vVRt25d1KlTB8uXL9dpf6tWrbB+/XqMGzcObdu21blAYrt27cQwo/Xw4UMcOHAAP//8M+Li4tCrVy9s2bIFq1evxltvvYWXX34ZHTt2xOTJk2FnZye29cmLCmqnA4Wn92/atAmZmZk4cuRIqUHoSdoB2c8yatSoUucVDTpRUVHYsWMH0tLSkJ2dLZ4p9fjxYyxfvhzm5uY6Y6lKuwv7tm3bxKtiFz1atGLFCsTExCAjIwPZ2dlo1qwZwsLCxFt4jBs3Du+88w6io6PxzTffICYmBhEREejQocMzt5GoqmLYIaqGit7W4Gm03THPUr9+fRw9ehTNmjXTuW/V02iPLD3tppcAit3nCig8xfpJbm5uOHr0aIl3Pc/Lyyv2OuvWrcNrr72GyMhIcUDzhg0bcOzYMWzZsgUXL14UB2rb2toWG1w9evRonTOMtBctNDMzw9ChQxEUFPTU7dIqa9gpq0aNGiExMRHNmjXDmDFjxAv5WVhYYP78+Th27JhOeCnttWvVqoVTp05hzJgxOgPWfX19kZ+fjyZNmqB9+/ZwcnIqtqyJiQmGDRuGwYMH48SJEww6VO3JhLIcfyUiSdPeH6msp5dXBRqN5qntfdZ8qdOGoLLeP41Iyhh2iIiISNJq7s8eIiIiqhEYdoiIiEjSGHaIiIhI0mr82VgajQYFBQWQy+UVuuoqERERVT5BEKDRaMp0774aH3YKCgpw8eJFQzeDiIiIKsDV1fWZFwCt8WFHmwZdXV3LfOVYIiIiMiy1Wo2LFy+W6RITNT7saLuuFAoFww4REVE1U5YhKBygTERERJLGsENERESSxrBDREREksawQ0RERJLGsENERESSxrBDREREksawQ0RERJLGsENERESSxrBDREREksawQ0RERJLGsENERESSxrBDREREksawQ0RERJLGsENERESSxrBDREREksawQ1RNqTUaQzehSnjeOrCOhVgHkjIjQzegOlBrNFDImQv1UQfWspA+6qCQyzH7x19xPT1TT62qfpo2sELYqC7PtQ7WUT91JP3iZ2UhfdWBYacM+GGovw9D1lK/XyzX0zNx9U6GXtZVk7GO+sEv6EL8MaMf+vysZNgpI34Y6g9rSSRN/ILmj5mqimGHiIj0hl/QVBXxeCMRERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUmaQcLOoUOH4OPjg1atWmHw4MFITk4GAISFhcHZ2Vn84+vrKy6TmJgIf39/eHp6Ijw8HIIgiPNOnTqFvn37wsvLCxs3bqz07SEiIqKqq9LDzq1btzBr1iyEhIQgNjYWjo6O+OSTTwAAly5dwrp163D69GmcPn0aUVFRAACVSoWgoCC0bt0aO3fuRHJyMiIjIwEAGRkZGD9+PPr374+tW7ciJiYGJ0+erOzNIiIioiqq0sNOcnIyQkJC0K9fP9SrVw8jR47ElStXUFBQgGvXrsHDwwO1a9dG7dq1YWFhAQCIjY2FUqlEaGgoGjdujODgYOzYsQMAsGvXLjRo0AATJ06Eo6MjJkyYIM4jIiIiMqrsF+zevbvO4+vXr6NJkyZITEyERqOBn58f7t27B09PTyxYsACNGjXC1atX4e7uDjMzMwCAs7Oz2PWVkJAALy8vyGQyAICbmxuWLVtW7nap1epS5ykUinKvT6qeVqeyYC3/xVrqz/PUknX8F/dJ/WEt9ae0WpanxpUedopSqVTYuHEjxowZg6SkJDRt2hRz5syBtbU1Fi1ahDlz5mDDhg1QKpWwt7cXl5PJZJDL5cjMzIRSqUTz5s3FeRYWFkhPTy93Wy5evFjidDMzM7Rq1ar8GydRCQkJyMnJqdCyrKUu1lJ/KlpL1lEX90n9YS3153lqqWXQsLNq1SqYmZlh2LBhMDY2xqBBg8R58+bNg4+PD5RKJRQKBUxMTHSWNTU1RW5ubrF52unl5erqyiRdBs7OzoZugmSwlvrDWuoH66g/rKX+lFZLtVpd6oGKJxks7Jw4cQKbN2/Gtm3bYGxsXGx+3bp1odFokJ6eDisrK1y7dk1nflZWFoyNjWFlZYWMjIxi08tLoVAw7JQBa6Q/rKX+sJb6wTrqD2upP/qopUFOPU9NTUVISAjmzp0LJycnAEB4eDhiYmLE55w/fx5yuRx2dnZwdXXFH3/8obO8SqWClZVVsXmXL1+Gra1tZW0KERERVXGVHnZyc3MRFBQEHx8f+Pr6IisrC1lZWXB2dsaKFStw4sQJHD9+HPPmzYOfnx/MzMzg6ekJpVKJnTt3AgDWrl2LTp06QaFQoEePHjh37hzi4uKQn5+P9evXw9vbu7I3i4iIiKqoSu/GOn78OJKSkpCUlIRt27aJ0w8fPox+/fph8uTJUCgUGDhwIIKDgwsbaWSEsLAwhISEYMmSJZDL5YiIiAAA2NjYIDQ0FIGBgTA3N4elpSUWL15c2ZtFREREVVSlh52ePXsiISGhxHkhISEICQkpcZ6Pjw8OHjyI+Ph4uLu7w9raWpw3cuRIeHt7IyUlBR4eHqhVq9YLaTsRERFVPwY9G6u86tevj27dupU4z8HBAQ4ODpXbICIiIqryeCNQIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIOEnUOHDsHHxwetWrXC4MGDkZycDABITEyEv78/PD09ER4eDkEQxGVOnTqFvn37wsvLCxs3btRZ3/79+9G9e3d4e3tj9+7dlbotREREVLVVeti5desWZs2ahZCQEMTGxsLR0RGffPIJVCoVgoKC0Lp1a+zcuRPJycmIjIwEAGRkZGD8+PHo378/tm7dipiYGJw8eRJAYUCaMWMGJkyYgA0bNuCrr75CSkpKZW8WERERVVGVHnaSk5MREhKCfv36oV69ehg5ciSuXLmC2NhYKJVKhIaGonHjxggODsaOHTsAALt27UKDBg0wceJEODo6YsKECeK87du3w8vLC8OGDYOzszPeeustREdHV/ZmERERURVlVNkv2L17d53H169fR5MmTXD16lW4u7vDzMwMAODs7Cx2byUkJMDLywsymQwA4ObmhmXLlgEArl69iq5du4rrc3Nzw5o1a8rdLrVaXeo8hUJR7vVJ1dPqVBas5b9YS/15nlqyjv/iPqk/rKX+lFbL8tS40sNOUSqVChs3bsSYMWNw69Yt2Nvbi/NkMhnkcjkyMzOhVCrRvHlzcZ6FhQXS09MBAFlZWTrLFZ1XHhcvXixxupmZGVq1alXu9UlVQkICcnJyKrQsa6mLtdSfitaSddTFfVJ/WEv9eZ5aahk07KxatQpmZmYYNmwYVqxYARMTE535pqamyM3NhUKh0JmnnQ7gqfPKw9XVlUm6DJydnQ3dBMlgLfWHtdQP1lF/WEv9Ka2WarW61AMVTzJY2Dlx4gQ2b96Mbdu2wdjYGFZWVrh27ZrOc7KyssR5GRkZxaYDeOq88lAoFAw7ZcAa6Q9rqT+spX6wjvrDWuqPPmppkFPPU1NTERISgrlz58LJyQlA4ZGVP/74Q+c5KpUKVlZWxeZdvnwZtra2JS5XdB4RERFRpYed3NxcBAUFwcfHB76+vsjKykJWVhY8PDygVCqxc+dOAMDatWvRqVMnKBQK9OjRA+fOnUNcXBzy8/Oxfv16eHt7AwB69+6NvXv3IiEhAVlZWYiIiBDnEREREVV6N9bx48eRlJSEpKQkbNu2TZx++PBhhIWFISQkBEuWLIFcLkdERAQAwMbGBqGhoQgMDIS5uTksLS2xePFiAICLiwsCAgLg7+8PU1NTNGnSBKNGjarszSIiIqIqqtLDTs+ePZGQkFDiPHt7exw8eBDx8fFwd3eHtbW1OG/kyJHw9vZGSkoKPDw8UKtWLXHe9OnTMXDgQNy7dw+enp7FBjoTERFRzWXQs7FKUr9+fXTr1q3EeQ4ODnBwcChxnpOTkzj+h4iIiEiLNwIlIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJY9ghIiIiSWPYISIiIklj2CEiIiJJe+6wo1KpIAgCNBqNPtpDREREpFcVCjtKpRJz5sxBp06d0LZtWyQkJKBr1664dOmSvttHRERE9FwqFHZmzZqFO3fuYPHixTAzM4OlpSXGjBmD+fPn67t9RERERM/FqCILxcXFISYmBnZ2dpDL5ZDJZBgyZAjWrFmj7/YRERERPZcKHdlp1qwZIiMjAQAymQwymQynT59GixYt9No4IiIioudVoSM7s2fPRmBgILZs2YKsrCxMmzYNd+/exTfffKPv9hERERE9lwod2XFzc8PBgwfx4YcfYtq0aRg9ejT27t2LNm3alHkdGRkZ6NGjB27fvi1OCwsLg7Ozs/jH19dXnJeYmAh/f394enoiPDwcgiCI806dOoW+ffvCy8sLGzdurMgmERERkURV+NRzS0tLDBo0COPGjUOnTp1gaWlZ5mUzMjIQFBSEO3fu6Ey/dOkS1q1bh9OnT+P06dOIiooCUHh6e1BQEFq3bo2dO3ciOTlZ7EbLyMjA+PHj0b9/f2zduhUxMTE4efJkRTeLiIiIJKZCYScpKQlDhgzBvn37AABjxoxB//79cf369TItHxwcjAEDBuhMKygowLVr1+Dh4YHatWujdu3asLCwAADExsZCqVQiNDQUjRs3RnBwMHbs2AEA2LVrFxo0aICJEyfC0dEREyZMEOcRERERVSjszJ07F56envD29gYAbN26Fd27d8e8efPKtPyCBQsQEBCgMy0xMREajQZ+fn5wc3PD+++/j7t37wIArl69Cnd3d5iZmQEAnJ2dkZycDABISEiAl5cXZDIZgMIutvj4+HJvk1qtLvUP/etpdSrLH/oXa6k/rKN+cJ/UH9ZSf/RRpwoNUL5y5QpWrFghdl2Zm5vj7bffRr9+/cq0vIODQ7FpSUlJaNq0KebMmQNra2ssWrQIc+bMwYYNG6BUKmFvby8+VyaTQS6XIzMzE0qlEs2bNxfnWVhYID09vdzbdPHixRKnm5mZoVWrVuVen1QlJCQgJyenQsuylrpYS/2paC1ZR13cJ/WHtdSf56mlVoXCjrOzM6KiovDBBx+I06Kjo5/r1PNBgwZh0KBB4uN58+bBx8cHSqUSCoUCJiYmOs83NTVFbm5usXna6eXl6uoKhUJR4fbXFM7OzoZugmSwlvrDWuoH66g/rKX+lFZLtVpd6oGKJ1Uo7MydOxfjxo1DdHQ07O3tcefOHWRmZmL9+vUVWV2J6tatC41Gg/T0dFhZWeHatWs687OysmBsbAwrKytkZGQUm15eCoWCYacMWCP9YS31h7XUD9ZRf1hL/dFHLSsUdlq1aoUDBw7g6NGjSEtLw6BBg9CtWzdxQHFFhIeHo1WrVhg4cCAA4Pz585DL5bCzs4Orqyu2b98uPjc1NRUqlQpWVlZwdXXF7t27xXmXL1+Gra1thdtBRERE0lKhsAMUjo158oyq5+Hi4oIVK1agXr16UKvVWLBgAfz8/GBmZgZPT08olUrs3LkT/v7+WLt2LTp16gSFQoEePXpg/vz5iIuLg6enJ9avXy8OnCYiIiKqcNjRt8GDByMpKQmTJ0+GQqHAwIEDERwcDAAwMjJCWFgYQkJCsGTJEsjlckRERAAAbGxsEBoaisDAQJibm8PS0hKLFy825KYQERFRFWLQsJOQkKDzOCQkBCEhISU+18fHBwcPHkR8fDzc3d1hbW0tzhs5ciS8vb2RkpICDw8P1KpV64W2m4iIiKqPMoedli1b4vTp07CwsICLi4t4XRstQRAgk8lw5coVvTdSq379+ujWrVuJ8xwcHEo8pZ2IiIhqtjKHnUOHDokDkA8fPvzCGkRERESkT2UOOy+//HKJ/yYiIiKqyip8I1AiIiKi6oBhh4iIiCStQmFny5YtFbr/FBEREVFlq1DY+f7771/oWVdERERE+lKhsDNhwgR88803yMrK0nd7iIiIiPSqQhcVvHXrFgCgV69e4i0dtCZNmqSflhERERHpQYXCzp07d9C0aVM0bdpU547jRERERFVNhcLOokWLAAAajQb//PMPrK2ti11RmYiIiKgqqNCYnb///hsTJ06Em5sbOnfuDFdXV0ybNg0PHjzQd/uIiIiInkuFwk5oaCgEQcD27dvx22+/YcuWLcjLy0NoaKi+20dERET0XCrUjXX+/HlER0eLt42wsbHBJ598goEDB+q1cURERETPq0JHdjp06IB9+/bpTNu7dy86d+6sl0YRERER6UuFjuzcv38fS5cuxebNm2Fra4u0tDSkp6fD3d0dAQEBAIBNmzbptaFEREREFVGhsDNq1Ch9t4OIiIjohahQ2BkyZIi+20FERET0QvCu50RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpBgs7GRkZ6NGjB27fvi1OS0xMhL+/Pzw9PREeHg5BEMR5p06dQt++feHl5YWNGzfqrGv//v3o3r07vL29sXv37krbBiIiIqr6DBJ2MjIyEBQUhDt37ojTVCoVgoKC0Lp1a+zcuRPJycmIjIwUnz9+/Hj0798fW7duRUxMDE6ePAmgMCDNmDEDEyZMwIYNG/DVV18hJSXFEJtFREREVZBBwk5wcDAGDBigMy02NhZKpRKhoaFo3LgxgoODsWPHDgDArl270KBBA0ycOBGOjo6YMGGCOG/79u3w8vLCsGHD4OzsjLfeegvR0dGVvk1ERERUNRkZ4kUXLFgABwcHfP755+K0q1evwt3dHWZmZgAAZ2dnJCcnAwASEhLg5eUFmUwGAHBzc8OyZcvE5bp27Squx83NDWvWrCl3m9RqdanzFApFudcnVU+rU1mwlv9iLfXneWrJOv6L+6T+sJb6U1oty1Njg4QdBweHYtOUSiXs7e3FxzKZDHK5HJmZmVAqlWjevLk4z8LCAunp6QCArKwsneWKziuPixcvljjdzMwMrVq1Kvf6pCohIQE5OTkVWpa11MVa6k9Fa8k66uI+qT+spf48Ty21DBJ2SqJQKGBiYqIzzdTUFLm5ucXmaaeXtFzReeXh6urKJF0Gzs7Ohm6CZLCW+sNa6gfrqD+spf6UVku1Wl3qgYonVZmwY2VlhWvXrulMy8rKgrGxMaysrJCRkVFsuna50uaVh0KhYNgpA9ZIf1hL/WEt9YN11B/WUn/0Ucsqc50dV1dX/PHHH+Lj1NRUqFQqWFlZFZt3+fJl2Nralrhc0XlEREREVSbseHp6QqlUYufOnQCAtWvXolOnTlAoFOjRowfOnTuHuLg45OfnY/369fD29gYA9O7dG3v37kVCQgKysrIQEREhziMiIiKqMt1YRkZGCAsLQ0hICJYsWQK5XI6IiAgAgI2NDUJDQxEYGAhzc3NYWlpi8eLFAAAXFxcEBATA398fpqamaNKkCUaNGmXITSEiIqIqxKBhJyEhQeexj48PDh48iPj4eLi7u8Pa2lqcN3LkSHh7eyMlJQUeHh6oVauWOG/69OkYOHAg7t27B09Pz2IDnYmIiKjmqjJHdrTq16+Pbt26lTjPwcGhxNPWAcDJyQlOTk4vsGVERERUHVWZMTtERERELwLDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSRrDDhEREUkaww4RERFJGsMOERERSVqVCzthYWFwdnYW//j6+gIAEhMT4e/vD09PT4SHh0MQBHGZU6dOoW/fvvDy8sLGjRsN1XQiIiKqgqpc2Ll06RLWrVuH06dP4/Tp04iKioJKpUJQUBBat26NnTt3Ijk5GZGRkQCAjIwMjB8/Hv3798fWrVsRExODkydPGngriIiIqKqoUmGnoKAA165dg4eHB2rXro3atWvDwsICsbGxUCqVCA0NRePGjREcHIwdO3YAAHbt2oUGDRpg4sSJcHR0xIQJE8R5REREREaGbkBRiYmJ0Gg08PPzw7179+Dp6YkFCxbg6tWrcHd3h5mZGQDA2dkZycnJAICEhAR4eXlBJpMBANzc3LBs2bJyv7ZarS51nkKhqMDWSNPT6lQWrOW/WEv9eZ5aso7/4j6pP6yl/pRWy/LUuEqFnaSkJDRt2hRz5syBtbU1Fi1ahDlz5qBFixawt7cXnyeTySCXy5GZmQmlUonmzZuL8ywsLJCenl7u17548WKJ083MzNCqVavyb4xEJSQkICcnp0LLspa6WEv9qWgtWUdd3Cf1h7XUn+eppVaVCjuDBg3CoEGDxMfz5s2Dj48PmjdvDhMTE53nmpqaIjc3FwqFQmeednp5ubq6MkmXgbOzs6GbIBmspf6wlvrBOuoPa6k/pdVSrVaXeqDiSVUq7Dypbt260Gg0qFevHq5du6YzLysrC8bGxrCyskJGRkax6eWlUCgYdsqANdIf1lJ/WEv9YB31h7XUH33UskoNUA4PD0dMTIz4+Pz585DL5XB2dsYff/whTk9NTYVKpYKVlRVcXV115l2+fBm2traV2GoiIiKqyqpU2HFxccGKFStw4sQJHD9+HPPmzYOfnx86d+4MpVKJnTt3AgDWrl2LTp06QaFQoEePHjh37hzi4uKQn5+P9evXw9vb28BbQkRERFVFlerGGjx4MJKSkjB58mQoFAoMHDgQwcHBMDIyQlhYGEJCQrBkyRLI5XJEREQAAGxsbBAaGorAwECYm5vD0tISixcvNvCWEBERUVVRpcIOAISEhCAkJKTYdB8fHxw8eBDx8fFwd3eHtbW1OG/kyJHw9vZGSkoKPDw8UKtWrcpsMhEREVVhVS7sPE39+vXRrVu3Euc5ODjAwcGhchtEREREVV6VGrNDREREpG8MO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaQw7REREJGkMO0RERCRpDDtEREQkaZIJO4mJifD394enpyfCw8MhCIKhm0RERERVgCTCjkqlQlBQEFq3bo2dO3ciOTkZkZGRhm4WERERVQGSCDuxsbFQKpUIDQ1F48aNERwcjB07dhi6WURERFQFGBm6Afpw9epVuLu7w8zMDADg7OyM5OTkMi2r7e5SqVRQKBQlPkehUKBFQyuYKGT6aXA11KR+bajVaqjV6udaD2vJWuqTPmrJOnKf1CfWUn+eVUvt9LIMW5EJEhjcsnjxYuTl5WHevHnitI4dO+LAgQOwsrJ66rIqlQoXL1580U0kIiKiF8DV1RUmJiZPfY4kjuwoFIpiG2pqaorc3Nxnhh0jIyO4urpCLpdDJqu5CZqIiKg6EQQBGo0GRkbPjjKSCDtWVla4du2azrSsrCwYGxs/c1m5XP7MREhERETVlyQGKLu6uuKPP/4QH6empkKlUj3zqA4RERFJnyTCjqenJ5RKJXbu3AkAWLt2LTp16lTqgGMiIiKqOSQxQBkADh8+jJCQEJiamkIulyMiIgJOTk6GbhYREREZmGTCDgDcv38f8fHxcHd3h7W1taGbQ0RERFWApMIOERER0ZMkMWaHiIiIqDQMO0RERCRpDDs1yIMHDwzdBMlgLfXnn3/+MXQTJIF11B++v/UvMTERMTExBnt9hp0a4OrVq+jXrx+GDx8OlUpl6OZUa6yl/ly9ehV+fn6YNm0aCgoKDN2caot11B++v/Xv4cOHeO+99zB+/HgcPHgQFy5cMEg7GHYkbu/evZg1axbmzJkDLy8vfPHFF4ZuUrXFWurP5s2b8fHHH2Ps2LFwdHTEd999Z+gmVUuso/7w/f1i7Ny5E7169cL27dvRpUsXwx3dEUiSCgoKBEEQhHv37gnZ2dmCIAhCRkaG4OPjI1y/ft2ALat+WEv90dby1q1bQm5uriAIgnD58mVh9OjRwp07dwzZtGqFddQfvr/1T6PRCGq1WhAEQfxbEAQhKSlJmDdvnnDgwIFKbxOP7EiMRqMBAPHq0Q0aNICZmRny8/NhbW0Nf39/LF682JBNrDZYS/15spYODg4wNTUFALRs2RIdO3bEt99+a7D2VReso/7w/f1iCIIAmUwGubwwXsjlcrHW9vb28PLywtGjR6FUKiu1XQw7EiIIgriD/fDDD+jZsydOnDgBAOJdYcePH4+7d+/il19+MVg7qwPWUn+K1nLTpk148803cenSJZ3nDBs2DLdu3cLp06cN0cRqgXXUH76/XxyZTIZjx44hPDwce/fuhUajEWttamqKtm3bwsbGBj/99FPlNqzSjyXRC/Xnn38KPXr0EDp37izs2bNHZ572cO3PP/8s+Pn5GaJ51QprqT/nz58XevbsKXTs2FHYt29fic+JjIwUJk2aVMktq15YR/3h+/vFOHXqlNCxY0dh9erVwvDhw4UvvvhC+P3338X5arVaiIuLEz766KNK7SbkkZ1qTHtoUCs7OxsLFy7Eq6++iuPHj6Nfv37Iz88X52sP1/r6+sLGxoaDGYsQnriQOGtZcU/WUqlUYvLkyejSpQtOnDiBPn36QK1WF1uud+/eMDIyQlRUVGU1tUp7skaPHz9mHSuI72/9e/L7R+vIkSNwdXXFxIkTsWzZMjRt2hTfffcdsrOzARR2a7m4uMDJyalSj+4w7FRD2jeu9tCg9rG5uTnatGkDY2NjAMDy5csxa9Ys/PTTT0hLSwMA5OXlAQBmzpyJH3/8sdL7TasytVqNjIwMqNVq1vI5yGQyAEBqairy8/NhYWGBzp07w8zMDACwevVqLFq0CMeOHROvDaOt+fDhwxEdHS1+MNZE2vez9gt3165d+Pvvv2FpaQlvb2/WsQK0+6T2C9rc3Bzu7u58fz8H7ffP1atX8fDhQ3H6Sy+9JIZwe3t79OzZE1ZWVli5cqX4HGtra3Tq1Anp6emV1uXKsFMNad+4kZGRGDRoEGbOnInIyEgAwNixY/Hbb7+hV69eOHnyJF566SX8+OOP+PDDDwEU9pkWFBSgRYsW6NSpE5YsWWKw7ahK1qxZgx49emDSpEn4+uuvAbCWz2Pjxo0ICQlBRkYGAGDUqFE4deoUfHx8cOzYMdy/fx8rV64UB4Bqv9g7duwIJyenGv1Luuj7u127doiOjhbnjRw5knWsgLt37yIkJAQXL14UA8+7776LuLg4vr8rKD09HUOGDEFoaCgmT56MPXv2QKVSoV69epDJZEhISAAAWFlZ4d1338XRo0dx7do1cfkmTZqgbdu22LdvX+U0uNI6zEivtm7dKnTu3FnYtGmT8OGHHwq9evUSYmNjBUEQhBkzZgjDhg0T8vPzBUEQhL///lt4/fXXhbVr1wqCIIinqgqCIPTt21e4cuVK5W9AFfLtt98KvXv3Fo4fPy7ExMQIvXv3FuLi4gRBEIQPP/yQtSwHjUYjCIIgREdHCy4uLkJkZKRQUFAg3Lp1Sxg3bpwQGBgoPvePP/4Q+vTpI2zbtk0QBEHIz88XNBqNcOfOHeHdd98VkpOTDbINhqTRaITHjx8L7733ntCxY0dh69atOvOTk5OFwMBA1rGckpOTBU9PTyEvL0+clpmZKXz00Ud8f1fQ3r17hZEjRwq5ubnCvn37hAULFghffvmlkJmZKQwePFjYvXu3WFdBEITFixcLM2fOFATh3zFR2dnZwieffCLs3bv3hbeXR3aqqYMHDyIgIABvv/02QkJC0KlTJ8TGxgIAPD09ERgYCCMjI2g0GtStWxfvvvsuoqKioNFoxFNVtX744QdDbEKVERcXh8mTJ6Nz587o3LkzWrRogfT0dAiCgFdffZW1LAftUYl79+5BEATExsbixo0bsLe3R6tWrTBy5EjxuS1btkTPnj1x8OBBaDQaGBkZQSaToU6dOsjIyMDu3bsNtRkGI5PJYGFhgcTERHz22WcYPnw4Hj9+jMuXLwMAmjZtilatWuHNN98Ul2Edn04QBAiCgBYtWuDevXti91Tt2rXh4eHB93c5Cf/fzXrmzBkUFBTA1NQUvXv3xgcffICYmBjcvXsXQ4YMwZ49e3Djxg0Ahd2rbdu2xcOHD3Hv3j3xCOTq1avx888/Iycnp9i4Kn1j2KmG8vPz4eTkBGdnZwCAra0t8vLyxDfxkCFD0LNnTwD/9qs+fvwYVlZWyMzMBAAcO3YMHTp0QOPGjfHuu+8aYCsMT/vm8vHxgbm5OYDCvuTbt2/D1tYWMpkMI0aMYC3LQdtFYGpqigkTJkAmk2Hfvn0oKCjAtGnT0K1bN/G5JiYmyMjIgFwuF29zsHfvXnTv3h0NGzbE4MGDDbEJBqWtwwcffIBdu3bhu+++Q8+ePTF9+nRMnToVZ8+exdSpU9G9e3dxGdbx6WQyGQoKCnDt2jWcOnUKS5YswRtvvIF169ahZ8+efH+XwdmzZ7Ft2zYAEAdyt2jRAnK5HPfu3YNMJkP9+vUREBCADz/8EO+88w7y8/Oxa9cu3LhxAwqFAkZGRvjrr79Qv359AMCnn36Ks2fPYvny5XjjjTfEH0ovitELXTu9EMbGxujWrRvc3d2hVquhUCjQuHFjnD17VpyflJSEK1euwNnZGU2bNkVKSgpcXV1hbW0NADAzM8Pq1avRoUMHQ26KQWnfXL6+vqhTpw5UKhW++uorpKSkYN68eWjWrBkWLlyIvLw8nDx5Eq1atWItn0H7hZGUlIR69ephypQpmD17Nnr16oVXXnkFFy5cwD///ANXV1eYmZnhwYMHaNOmDUxMTAAU/p8sX74cnTp1MuRmGIz2Gi/u7u7Ys2cPtm7dilWrVkGtVuOXX37BnDlzsHnzZmRmZiIxMRFeXl4wNTVlHZ/h0aNHkMlkiIuLQ/v27eHl5YUFCxZAEAR4eXkhLS0NzZo1Q7Nmzfj+LsHp06excuVKDBw4UBwg37BhQ1haWuLcuXPo27cvAGDMmDH48ccfERMTg9DQUKxduxaTJ0/G5MmT8f3338PS0hK5ubkwNzfHhAkTUK9ePfEz40Vj2KmmvLy8dB6npKTAyclJfFxQUID9+/dj3bp1UKlUyM/Px+rVq8X5NfmN+6QGDRoAKDwqERAQgMDAQCQkJOA///kPFi1ahJkzZ+LAgQPYsGEDa/kMwv9fPdXS0hKWlpZwdHREu3btsHbtWlhYWMDKygq//fYbjI2NxcHL2gGhAMQPzZrOxsYGtWrVgpeXl7h/vfLKK0hISMCmTZswePBgfPPNN9i4cSPrWAaWlpbIzMzE4MGD0bVrVwCFg5aPHTsGZ2dnxMTEIDU1le/vUiQkJMDY2Bjz5s0TB2p36dIF+/btw59//gl3d3c0atQIADBt2jR899132LZtG+bOnYsvvvgC69atQ6NGjTBlyhTxKLr2c7eyMOxUc9qrUyoUCtSqVUuc7uLigq+++gq//fYbMjIy4OfnZ7hGVgPC/19RVfsG9PT0RH5+PhYtWgQTExN88cUXuHjxIu7du8daPkXRMTuOjo7iv/fv348OHTpg7dq1GDVqFM6fP49Hjx5hxIgRBmxt1fXyyy9jypQpcHNzE6fVrVsXrVu3RmpqKpo2bYrVq1cjPj4e//zzD+v4DCYmJmjSpAnu3bsnThsxYgS++OILfPzxx1ixYgXOnDmD9PR0vr+fkJqaihs3bmD16tUICgrCO++8g9atW0OhUMDPzw8//vgjDh06hICAAABAnTp1YGpqijt37sDBwQHz5s1DQUGBeJq/oTDsVHPaQ4BnzpzBkCFDAACLFy9GfHw8IiIixF8xQOHRHu1hctKl/ZK+c+cOrKysYGFhgTt37sDR0RG1atWCTCbDa6+9Jj6ftSyZIAjQaDQwMzPD/v37sXLlSrRs2RKjR4/GjRs3cP/+fbz88ss6Rx5Yy5Jpg05+fr74RZGQkICGDRsCKLyGib29vfh81rF01tbWaNu2LS5duoQePXqgbt26SE5OhpOTE4yNjWFiYqLT5cda/svBwQGzZ8/Gq6++imHDhmH27NniBStfe+01pKSk4Pjx43j06BEmTZqEGzdu4O7duzpHboyNjcWjvobC/00DKXq/kOd1//591K5dG7/99htmzJiBWrVqYe7cuQCgs4NJ9c2rr1qmpKRgy5YtOHPmDAYMGID//e9/GDJkSIlvUNayZDKZDAqFAoIg4Nq1a5g7dy569eoFAAgPDxcPYRclxVrqa5+MiIjA8ePH0bVrV6jVaty8eVP8Bf0kKdYReP5aCoIAa2tr9O3bF1FRUQgICMDQoUPx/fffw9XVVScwakm1lhX16quvAgCmTp2KXr16ITo6Whz47u/vjyZNmuCjjz5CVFQUHj58iClTpohnsmk/Pw0ZdABAJrzo871Ih3ZAMQBkZmbCysrqudd5//59dOnSBbVq1cKHH34onpZq6CT9or2IWv7111/YsmULUlNT4eXlheHDhz/3OqsDfdVS+8VUUFAAlUoFc3NznXVLXdFtVSqVsLCweK715ebmYv369Th//jxu376NsWPHYtiwYfpoapWnr32y6OdgRkYGtm3bhrS0NLi6umLo0KF6a29N8f333+Obb77B77//DuDfo2CpqalISUmBg4MDGjZsWOIPG0Ni2DGQzz77DHv27EFkZGSJvyzKIzU1FZcuXdLpGqhJXzD6rGVJ9HkUrqpjLfXjs88+w7lz57Bu3TrY2tpWaB1Fa/VkcJL6D5mi9LVPllazmvRZqQ8ajQaDBw9G9+7dERwcrNPNWpVJ/1OniomJiYGbmxsuX76MjRs36uULxcHBQQw62uts1IQ374uoZVHaa8bUhC/nF11LLanXMioqCm3btsWFCxcQHh5e4aAD6NZKe/KB9p5DNSHo6HuffLJm2ve31D8rS7thZ0XJ5XIEBwdj3bp1ePToUbUIOgCP7FSqtLQ0TJ06FZ07d8bUqVMBFN5sTtu3WVN+9VbEk7W5e/cupk2bxlpWwJO/cFnLinmyLsnJyXjnnXcwadIksSu56K/emnQ0pryerGVqaipCQkK4Tz6HF9HNX9SZM2fg4eFRbfZrjsKqRHZ2drCzs8PLL78MAAgJCYFMJkO7du0wbNgwmJiYVJsdp7JoP9Tkcjlyc3Px0ksvAQAaNWqEhg0bspbloK2HIAh48OABAKBevXqsZQXJ5XJkZ2fj1q1bcHFxQfPmzdGoUSPY2NgAAEJDQ2Fubo6OHTuie/fu4i0J+CX9r6Lv7wcPHuD7779HSEgIHBwc+Fn5nLRB50V1TXt4eACoPkcZ+a7TI+1ltLUHy6KionD06FEA/3YvjRw5EmFhYejduzcKCgpQp04d/PDDD+IFwbRfRjWdtpbaL4alS5eib9+++Pjjj/HTTz8BAIYPH85aloG2ljKZDNnZ2fjss8/g4+OD6dOnY/369QBYy7LQ1rGoJUuWYM2aNbhz5w6Awjtpz58/H927d8fjx4+RkZGBVatWYdGiReIy+u5WqI6efH8vWbIEXbt2xY0bN5CdnQ2An5XPq7K6pquNF3iT0Rrl8uXLwqVLlwRB+PeOrh988IHg5+cnZGdnC4JQeEfjnJwcYfTo0cLEiRPFZf/++2+hdevWwsGDB3XWmZmZWUmtr1qK1lKj0QhffPGFMHDgQGHXrl3CrFmzhLZt2wp//fWXIAiCMGrUKNbyKZ6s5cyZM4X3339fuHLlirBnzx7B1dVVSE9PFwRBEN566y3WshRF6ygIgqBWqwVBEIRVq1YJHh4ewk8//SSoVCrh1q1bwvDhw4VPPvlEEITCz4ILFy4ILi4uwsWLF3XWWfSO0DVJ0Vreu3dPGDVqlPD6668L58+f13leTk4O98ky0u6PWnfv3hWGDRsmrFixQpxW9A7uTz6/JuCRHT2Jjo7Gzz//DODfw4dZWVm4evUqIiIiAPz7S8Tf3x+BgYEAAJVKhbp168LPz0+8o64gCAgODsbmzZvFAYk1SXR0NA4cOACg8KZ8R44cQUhICAYOHIiPP/4Y7du3R2RkJABg6NChrOVTFK2lUqnEhQsXMHfuXLi4uKBr165o06YNrl27hpycHO6XT1H0/Q38e0QiNTUVCoUCCQkJSE5Oho2NDXr27IlRo0YBKHzPu7m5oWfPnuLngCAIGD16tLgP1zRF90kjIyPI5XJ8/fXXaNu2LZKSknDmzBk8evQIL730EoYMGcJ9sgy0XapaJQ2Z+OSTT7B582aoVCrI5fIad1SMYec5Fd1hunTpIk779ddf0bRpUyxevBirV6/GX3/9BaDwpnJ+fn5wc3ODIAhi37NcLoe5uTmys7Mhk8ng6+sLc3NzyZ8pUFTRWmqv/GxmZoZXX31VvCnfSy+9BDMzM7EuQ4YMYS1LUFItLS0t0bdvX/Fuzg8fPoRSqcTLL78MExMT1rIEpb2/td3SjRo1wpAhQ/Do0SOcPHkSADBu3Di0atUKAIpdc0ilUkEmk2HgwIF4+PBhJW+NYZW0T9rY2MDZ2RmrVq3CF198AX9/f3z66acYM2YMzp8/D39/f+6TJXiySzUxMRHjx49HamqqGPrYDaiLYec5aQdnZWVl4fLly+K0jh07YvTo0Rg8eDBcXV2xdOlSAP++4c+cOYOffvoJV69eRUZGBhITE9GpUyfxQkx9+/aFi4sLsrKyDLBVhlG0lvHx8QAKxzqNGjUKbdq0gVqthomJCRQKhc49bljL4kqqpSAIePvtt9GmTRtkZ2dj6dKluH37NiZOnCj+ej5z5gyioqJYy/9X2vtbe4Xdc+fOoX379ujTpw/i4+Nx8+ZNAMCxY8cQFxeH27dv48GDB3jw4AHatWsn3pV8xIgRcHd3rzF1BEquJVAYDi9cuICzZ8+Ktxjp2LEjJk2ahH/++Yf75BOuXLmCxMREAP9+n6SmpuLu3btwcHAQ6+zu7o42bdqgRYsWWLlyJWbPno0ffvgBhw8fxqFDhwD8+3/y6NEjA2xJ5eLZWM9J+P8zAlxdXVFQUCBeTdLY2Fi8C/nnn3+OPn36YNSoUWjfvj2AwmQeGxuLtWvXIjMzE05OTuJl9bXr9PT0rFFnbjxZy/z8fJiZmaFly5bic9RqNZRKJbp16yZOYy2LK22/rFOnDoDCI2YLFiyATCZDeno6xowZg//+97/o2LEjjh49inXr1rGWKL2O2tN6GzZsCLVaDW9vb8TFxWHDhg2QyWTivdXu37+PtLQ0tGrVCt7e3jrr9PLyqjZnsuhDabU0MTGBl5cXXnnlFdjZ2QEAPvroI+zbtw+7d++Gl5cX98kioqOjYWpqitatW4vTbGxs0KhRI6SlpaFu3booKCiAubk5/P390bx5cwDFuwF79uwJQRAQEhKCFi1aIDAwUNJHxxh2npP2w0qj0eDmzZswMjIS34ByuRxqtRqOjo4YPnw4Fi5ciB07dgAovIFa+/btce7cOfHxk+usKW9erSdr+eTN4wRBgEKhQE5Ojs6XhIeHB1599VWcOXOm2A07Wcvi+6V2fu3atQEUdm8FBQUhKioK77zzDpYuXYqzZ88C4H5ZWh21Xwq3bt2CnZ0d8vPzcerUKVy9ehVvvPEGZs+ejaysLPz222+oVasWXn/99WLrrElBByi5lkDhTTo/+ugjMeho91MfHx/ExsZi9OjR3Cehe52mol2qMpkMqampuH37Ns6fP4+ff/4Zly5dwvvvvw9fX1/Uq1cPBQUFJXYDmpubw9fXF+np6ZIOOgC7sZ6b9jDigAED8PvvvyMxMVHnQ0z7708//RQ3b94UByVqNBqYmpritddeE9+8NXmAHVC2WmZmZuLmzZvo2rUrcnNz8f7772PBggXiXYtZy0LPqiUAXLx4Ufz3w4cPxZv9mZiYcL/8f6XVsaCgADk5ObCzs8O8efPQqVMntGnTBm+99RYA4ObNm7CyskK/fv3EoFOT6wiUXEstOzs7CIKAe/fuQSaTQaPRICEhQTx6wX2y9G5AoHAsY1paGk6fPo0+ffpg/Pjx2L59OyIjI3H69Gns2bMHV65cqdHdgAw7z0kmk0GtVsPCwgKDBg3CqlWrdAaPaQcoyuVyBAYGYsGCBeJo+CdJPVk/y7NqqdFokJaWhgYNGmDp0qXo2LEjBEHA5MmTi62LtXx6LZOTk7FmzRoEBATg888/x//+9z+4urqWuK6aXMvS6mhkZAQzMzNkZmaiYcOG2LBhAz799FO89dZbaN26NRwcHIqtqybXEXj2PhkREYH58+cjPDwc4eHhuH37Nry8vEpcV02spTYsurq6wsjISPxeAQrDnyAIeP/999GnTx/4+/vj9ddfx/Xr13H79m0cO3YM06dPx4ABA2BsbKzTDQgAnp6e4i1JpIq3i9CDoocXp0+fjnbt2qFz585o3rw5CgoKoFAoxPmJiYl45ZVXDNncKu1ptQQKB9COHj0ar7zyCmbPno0OHToA4OXjS1JSLV977TW0aNECQGHgOXr0KJKSkuDv7y9eEZV0lVTHDh06wMXFBQ8ePICNjY14ZktN65oqr5Jq2bFjR7zyyiu4d+8eTp06hYMHD+Lhw4eYOnUq98kSbNmyBdevX0doaKg4diw+Ph7vvfceVqxYIR79OnPmDAIDA3Ho0CFYWlri9OnTxbr5axKGHT3RftkmJyfj119/xf79+/Hpp5+iadOm4v1ctG90fjE/XWm1bNasGR4/foyUlBR4enqKzy06hoJ0lVbLJk2awMzMTOe52o8CfmEXV1odHR0dxVuYFMXgU7pn1VKlUolnrXGf/Jd2n1IqlRg9ejSWLFki/nBOTU3F6tWrAQDh4eEAgCNHjuA///kPVq9ejQYNGuisqybe6Z1h5wXZvHkzrly5gkePHiEwMBBt2rQxdJOqrc2bN+Py5cvIysrC2LFjxVpqz+agsittv2QALx++v/WntFrWxC/kZ9HW5LvvvsP58+exbNkyMRgeP34cK1euhKmpKVq2bInt27cjICAA06dPZ1gEw47ePfmLbv/+/fD29oaFhYUBW1U9sZb6w1rqB+uoP6xl+T2tGxAoPMLz66+/IiUlBX379hVPOiCGnReGv5T1h7XUH9ZSP1hH/WEty6e0bsDGjRuLZ1gVfS5QM07NfxaGHSIiomqKXdNlw7BDRERUzbAbsHwYdoiIiKopHsEpG4YdIiIikjTGQSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iIiKSNIYdIiIikjSGHSIiIpI0hh0iqjFmzpyJVatWPfU5kZGRePvttyupRURUGYwM3QAioqpkwIAB6N27t6GbQUR6xLBDRFSEiYkJTExMDN0MItIjdmMRkcGdPXsWfn5+cHd3x9ChQ5GUlAQAuHr1KgYMGIAOHTpg4cKF6NOnDyIiIgAAsbGxGDhwIDw8PPDJJ59ApVKV6bXu3LmDIUOGwNPTE2FhYSgoKNCZX1I31qpVqzBz5kysXr0aHh4e6NGjB86ePauHLSeiysCwQ0QGpdFoMHXqVPj6+uLQoUPw9PREeHg4AODTTz9F//79sWnTJuzYsQOff/45Bg0ahJs3b2LChAkICAjAjh078Oeff2L9+vVler09e/ZgypQpiIiIwJEjR7B169YyLXfs2DHcvn0bUVFRaN++Pb788ssKbzMRVS6GHSIyuKioKIwbNw5paWl49OgRrl+/DgC4cuUK+vTpAxcXF7Ro0QJpaWmwsrLC3r170bJlSwwbNgyOjo4YNWoUjhw5UqbX6tmzJ7p37w4XFxe89dZbOHDgQJmWUygUmD9/PhwcHODn54e0tLQKby8RVS6O2SEig5LL5fj++++xY8cOODg4wM7ODhqNBgDQuHFj/PHHH7C2tsaNGzfg5OQEAPjrr79w5coVeHh4AADUajXMzc3L9Hr29vbiv+3s7HD//v0yLde2bVtxLI+xsXGZt4+IDI9hh4gM6vfff8eOHTuwd+9e1K1bF8eOHUN8fDwEQYCTkxPCwsIwZ84cvPXWW3BxcQEANGzYEN27d8fHH38MoLArLCcnp0yvV/SITHp6OurVq1em5SwsLMq5ZURUVTDsEJFBZWVlAQAePXqEGzduYNGiRRAEASkpKThz5gz+97//wdLSEnZ2duIy2nE8N2/eRIsWLfDtt9/i7NmziIyMfObrHTx4EL/++isaNGiAH3/8EaNGjXph20ZEVQPDDhEZVJcuXeDt7Y033ngD9vb2GD58OJYtWwYrKyvUrVsXb7/9Nh4/fgwjIyP4+flh/vz5aNy4McLDw7F48WKkpqbCzc2tzAOGe/TogS+//BK3b99Gv379GHaIagCZIAiCoRtBRPSk7du3Y//+/QgLC8NLL72EhIQEBAYGIi4ujl1KRFQuPLJDRFVSx44dsXv3bgwYMAB5eXlo1KgRQkJCnhp0zpw5g6CgoBLnDR8+HB999NGLai4RVWE8skNEkpGXl1fq2VUWFhaoU6dO5TaIiKoEhh0iIiKSNF5UkIiIiCSNYYeIiIgkjWGHiIiIJI1hh4iIiCSNYYeIiIgkjWGHiIiIJI1hh4iIiCSNYYeIiIgk7f8AXXreG5pAX9gAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:43.689092Z",
     "start_time": "2025-04-20T03:53:43.448488Z"
    }
   },
   "cell_type": "code",
   "source": [
    "corr = data[['age', 'income', 'credit_score', 'price', 'items']].corr()\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
    "plt.title(\"变量相关性\")\n",
    "plt.show()"
   ],
   "id": "3c99846c031fa79f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:45.238864Z",
     "start_time": "2025-04-20T03:53:43.689092Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sns.scatterplot(x='age', y='credit_score', data=data)\n",
    "plt.title(\"年龄与信用分数关系\")\n",
    "plt.show()"
   ],
   "id": "acff75235afa2b59",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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yhYWFPP/88zz00EM0aNAg5J6FCxfy448/Mm7cuJDZK5HzkQIakVrMbDaHVNs9E4PBgNlsDizz+Hw+7rvvPi688EIaN27MtGnTAjVL2rRpw5/+9CcGDx6M0+nk9ddfD5mFGTVqFKNGjTqr9y4qKuL48eNnFdCcbofR6a5v3LiRxMTEsLNR58Ln8/HMM8/wn//8hxkzZoQsh+3evZs+ffqEfa7BYODVV19l/fr1rFq1io8++ojY2FhWrFjBkCFDSEpKCgSLXq83MPsVzt69e3nyySf59ttvmTt3Lo0bN6Zx48ZMnTqVhx9+mM8++4xRo0bRr1+/kMDm+PHj/OUvf8FsNpOfnx/29a1WKwsWLGDz5s384x//OGNwJRLtdDilSBTzer1n3Pnj8Xh4//33efrpp/n973/P5ZdfzrZt2wJJt3fccQcPPPBA2Bmgc1V6wGS4GY7yBzb6fD4+/fRT2rRpE/K/0ro6ZS1ZsoQ+ffqEBDpld0aVys/PDxtIfPvtt/zlL3/hzTff5PHHHw8ELk6nkxdffJFx48aRlZVFly5dTvsZGzVqRPPmzdm9ezfXX389L7zwAl9//TVHjx7l6aef5qWXXmLSpEls3LiRe++9N2Tp65tvvmHixIn069cPl8vFu+++S4cOHQLXb7jhBubOnYvJZGL8+PH06NEjqB7Ptm3b+NOf/kTDhg2ZP39+UIDn9XoDAenAgQN5++23OXDgAL///e/Pun6PSLTSDI1IFCvd0luR0u3EycnJNGzYkPr16/PHP/4xKMC46aabKC4u5t5772XZsmXEx8efdR+OHTvG9OnT8Xg8bNiwgSZNmgSSb8sqfb/S/pYWzyutIFzWX//616Bk5U2bNrFhwwbmzZsXcm9xcXFgeW3btm0MGTKEkydPhmw9P3bsGPfeey9Wq5U5c+bQs2fPwLX4+Hi+++47Dh06xAMPPBA2edbtdjN37lw+/PBDjh49ytixYwM7h9xuN8OHDycmJoY33niDtm3bkpqaysiRI+nfvz8LFy7EbreTn5/PE088QVFRERMnTqR///5hZ6q6d+/OsmXLeOutt3jzzTeDDrp85ZVXaNWqFdOmTSM2Nha/38+LL77IgQMHOHDgQNCM3iWXXMKbb77Jk08+GXZZSuR8okrBIlHslVdeYfbs2ZU+jqC8wsLCsEm6Z3LHHXdw8uRJ2rdvz+233x5y9hCULOVcd911vPPOO2HPSarIDz/8wPvvv8+DDz4Ycu2LL74InDDt8/l4+eWXueyyy0IScgEOHDhAcnJypSvoliYx9+vXL2iX1rZt21izZg1Dhw4Neu0DBw7w7bffct111wXajh8/Tv369c+45FbK7/cHLQOWVoQuu939qaee4ptvvqFnz56MHj26wh1kIucrBTQiIiIS9ZRDIyIiIlFPAY2IiIhEPQU0IiIiEvUU0IiIiEjUqxPbtn0+Hx6PJ1DuXURERGq/0jpTZrP5jDsD60RA4/F4yMzMjHQ3REREpBI6dep0xnILdSKgKY3qOnXq9Isrof4SXq+XzMzMiPdDzo3GLTpp3KKTxi06Vde4lb7u2dRtqhMBTekyk8lkqhVfkNrSDzk3GrfopHGLThq36FRd43Y26SJKChYREZGop4BGREREop4CGhEREYl6CmhEREQk6imgERERkaingEZERESingIaERERiXoKaERERCTqKaARERGRqKeARkRERKJenTj6QERERKrHvtwCThV58DRoxY4jThJsZpo3iKvxfiigERERkUrZk+NkwnuZrM/KCbSlO+w8PbATF9rja7QvWnISERGRc7Yvt4BHygUzAOuycnjkvUz25RbUaH8U0IiIiMg5O1XkYV25YKbUuqwcThV5arQ/WnISERGRMyrNlckrLCYp1oLT5SHOamJYeipdW9TH5fFhs5jYlJ3L7HW7yVdAIyIiIrXJnhwnj76XydoyMzJv3dmDaYO6Mmf9bqavzgq0pznsTBvUlcTYmg0xtOQkIiIip3UgtyAkmAFwur3MWb87JIdmfVYOc9fvJsZUsyGGZmhERETktE65PHyVfYJRfRxBS0tJseaQYKbUuqwcijy+Gu2nAhoREREJUjZfJtFmZuHwy9l68GTQPUXFFQcsTpdyaERERCRCsnOcrM86RuN6NlweH063l8Mni7ioURy3z/2SArcXgDf/0qPC10mw1WyIoYBGREREgJJ8mYMni1iReTBoS3aGw86oPq2564qL+PuqHQB8uiuHdIc97NbtjNbJJMYoKVhEREQiwOX1MX31jpAgZW1WDtNXZ3FV2yaBttnrdjM0LZWM1slB92Y47Ewe0JELavj4A83QiIiI1FHla8v4IWQ3U6m1WceYYDQEHhe4vYxZsJl3R/TC74dThcUkxlpIjNBZTpqhERERqYP25jhZu/0oB04UkuN0c+BkEQYgzmo67XPKF8srcHsxAJc0jseU+yOXNI6PSDADmqERERGpcw7mFnAgXK5M62SmDerKmAWbA8m/ZcXFBAc7Ga2TqVfDuTKnoxkaERGROqbodLkyO44xd/1uhqWnhjwnzWHHZv45oMloncwzAzqSEqEZmfJqR1glIiIi1apsvky9WAtdWjbgq+wTITMx67JyuKe3I+Q4g1FXtsZqMvD2XT1JtEUuV+Z0FNCIiIic5/bkOJnwXmZQZd/SM5fCLS8ZgFm3dcPl8RFjNnIkr4gL6tswAZel2mu282dJAY2IiMh5bF9uAY+UC2aAwONh6alBszEADROsQTuXUpPj8fn8YK69mSq1t2ciIiLyi+W7PGGL30FJUNO1Rf2gtjSHHYvRiMfnw+sHj8/HkVNFmIwGmtWiJabyNEMjIiJyHilfWyb/DGcqucocIlmaK2MwgNloxGT0YjYaad4grlbly4SjgEZEROQ8cTTHib/MYz9nPlOpVXI8MwZfSozZyOG8IoyGkuWbdin1qrOrVU4BjYiIyHngaG4BBX6YsCQ4X+bZgZ3IcCSzNutYyHPSHHYM5doMBoipxbkypxN9PRYREZEQLp+fR5aEJv8+teJ7Rlx5MRmO4N1JaQ47t6el4vP/vORkMBhoWs9Wa2rLnAvN0IiIiESp8rVlwiX/Fri93DHvSxYOv5yhp4oCW7E37z3BW5/t4ZG+7UlOsJJos9CmaWKtz5U5HQU0IiIiUWhvjpP9JwuJjzHj81Nh8m+B28upomLumPdloC3dYefJ/h2xGg21trbMuVBAIyIiEmUO5Bbg9fmD2hJizIzq42D2ut1hz2FqEG9l2eg08ou8JNhMOF1eTAYwG8tn0UQnBTQiIiLRxufH4/fz0uqsoJyZ9NNU/01z2AkXt1iNBppE6RJTeQpoREREarnytWXirCaeXLolJAF4XZjqvz8n//rL1ZaxcsF5EsyAAhoREZFaLTvHyfqsYzSuZ8Pl8eF0e2lW33ba6r/rsnJ46Pp2tE+pF0j+nf/ZHh7r257URgk13Puao4BGRESkljqQW8CxfFdIe06+u8Ln5Ra4GfHmJqBkhmZ0n9ZRWVvmXETk073zzjtcccUVdOnShVtvvZW9e/cCsH37dm666Sa6d+/OlClT8Pt/Tnj6/PPPuf766+nRowdz5syJRLdFRERqVLHPh9vjY3nmQe6Y9yUj3tzEsLlfnPF5jRJimDH4Umbd1o1+nZrRLMlWq89hqgo1HtBkZ2fz8ssvM2PGDFauXEmLFi14+OGHcbvd3H333XTo0IHFixezc+dO3n33XQCOHz/OPffcQ9++fVm4cCHLli1j48aNNd11ERGRarcvt4CtB/P4bFcOPh+89ElWSK7Mp7tySHeE32pdmgCcnGClWf1YMi5JpqU9via6HlE1vuT0/fff06VLFzp06ADATTfdxNixY1mzZg35+fmMHz+e2NhY7r//fiZOnMhNN93E0qVLady4MSNHjsRgMDBixAgWLVpEz549z+m9vd7QbWw1qfT9I90POTcat+ikcYtOdX3cjp8oCjqLqaDYGxLMAMxet5tpg7piMBhYu+PnIw1KE4AxwK9b1g+0V/ffZ3WN27m8Xo0HNA6Hg40bN7J161aaN2/O/PnzSUtLY9u2bXTp0oXY2FgA2rRpw86dOwH44Ycf6NGjBwZDyZ6zzp0788ILL5zze2dmZlbdB/kFaks/5Nxo3KKTxi061cVxa9mqbchZTG/fdXnYewvcXsYs2Mzie3px4ERhcPXfz7OZ1K89X3/9dQ32vkQkxy0iAc21117LgAEDAGjevDnvvPMOr776Ks2bNw/cZzAYMBqNnDx5kvz8fC6++OLAtYSEBI4cOXLO792pUydMJtMv/gyV5fV6yczMjHg/5Nxo3KKTxi061eVxO3iiKORgyXqxp/81XeD24vL4gqr/ZjjsTB7YiSb1bTT51a+qs7tBqmvcSl/3bNR4QPPtt9/yySef8Pbbb3PRRRcxc+ZM7rzzTnr27InVag26NyYmhqKiIkwmU9C10vZzZTKZasUXpLb0Q86Nxi06adyiU10Yt/K1ZfwQsrwUZzGd9qTsDEcyyXEWVo7N4FRRMYk2C4k2c0TPYorkuNV4UvDy5cvp27cvXbp0ITExkXvvvZe9e/eSlJTE8ePHg+51Op1YLJaQa6XtIiIi0ehQjhN/maML/IDNbCQ5Ifgf9vuPF/DUgI4hJ2WXzMR0pNDnpV1KPS5LtdMupV7UHixZFWp8hsbn85Gbmxt47HQ6KSwsxGw2B6337d27F7fbTVJSEp06dWL58uWBa99//z1NmjSpyW6LiIhUiaO5Bbj9sG7nMZqUKZZ3+GQh8+/syS2vbeTYT3Vm1u8+zpFv9vN4/w54vH5OFRaTGGvBbDIw45MdjL7qkgh/mtqjxmdounXrxqpVq5g7dy7Lli1jxIgRNGrUiFtvvZX8/HwWL14MwCuvvEKvXr0wmUz06dOHTZs2sWHDBoqLi5k5cybp6ek13XUREZFfzO3zc/hUEeGOhDxZ4OZ//tAl8Hj2ut1c0yGFiUu3cP0/1vLHVzdy/T/WMnHpFkZc2bpOz8iUV+MzNNdeey07d+5k3rx5HD16lNatWzN9+nQsFguTJ09m3LhxTJ06FaPRyBtvvAFAw4YNGT9+PMOHDycuLo7ExESee+65mu66iIhIpZTNl2kQbyE+xszyzINBOTNpDjujr2zNhfafg5QCt5cFn+3hmYGdKHB7yasluTK1UY0HNAaDgZEjRzJy5MiQa1dddRWrVq1iy5YtdOnShQYNGgSuDRo0iPT0dHbt2kW3bt2Ijz//iwSJiEj0K38Wk9lk5IdDeWzOPhF0X2lw89SNHflgbEZgecnj9fH3VT9w39VtaJtSLwKfIDrUurOcGjVqRO/evcNea9GiBS1atKjZDomIiFTSgdwCDp4sYkXmwaDDJNMcdqYN6sqYBZspcP9cPG59Vg6FxV76TlsXaEt32JnYvyNWY7hFKilV6wIaERGR84XL62P66h0hJ2OXzsYMS09l+uqsoGsFLi/LRqeRX+QlwWbC6fJiNRloqiWmCp3fR2+KiIhEUFGxj7Vhji6AkqCma4v6Ie2JsWbMRiMmI5iNRpo3iKVFHTiL6ZfSDI2IiEgVKV8sz+nyVHi/2+sLepzhSCbOYqoTh0lWNQU0IiIiVWBPjpMJ7wUfXbBkRFqFz2mUEBP4c4bDzpP9O2BWrkylKKARERH5hfblFjBx2Ra6tmzAsLRUXB4fNouJWKuRNIc97InZaQ47DeOtLLiz50+5Mh6sJgPNlCtTKQpoREREKiFoeSnOwi09LmTO+t1BSb5zh3bn9rRUgJCaM7enpXKiwF0mVyZOtWV+AQU0IiIi52hvjpP9JwuJjzHj8wN+OJJXFFJbxuv3M2bBZoalpwZmbmLMRjbvPcGYBZt5b0Qv2jRVbZmqoIBGRETkHBzKLcDl9fHy6qygHUzhasts3nuCri3rh2zNBshonUyi5fw+Ubwmadu2iIjIOXD7/ExcuiVkO/b6rBzmrN/NsPTUQNvsdbu5PS2VjNbJQfdmtE5m8oCOWJQAXGU0QyMiIlKB8luxMVBhbZlhaT8HNAVuL2MWbGbF6HSKPL7AcQYJVhM2oJFyZqqMAhoREZHTKH8Ok9PtpZ6t4l+d5WvL/Lrlz+cSlh6xbTAaFMxUMQU0IiIiYezPLeBYviukPSGm4l+d5WvLjOrjwGo2ktooocr7KD9TQCMiIhKGx+fD7fGxPPNg0Jbrj+7/zRlry8wYfCkxZiNH8opISbJxgWZjqp0CGhERkZ+UzZdpnBjDS59khQQux/PdFdaWyS1wk5xgJdFmoU3TRNWWqSEKaERERICjOU78ZR4XFHvDzsIYjQbVlqmFFNCIiEiddzS3gCI/7M/7uVhegcsb9t51Wcf4dcsGp60tc6YcG6keqkMjIiJ1XrHPj9vvZ/dRJ0fyXOQWuIm1mhjVx0GcNbj43atrdnF7eqvQ2jKOktoyypeJDIWRIiJS55SvLRNvNZFzqihoyenIKRcXJNl4+ZZLGTl/U6D6b4Hby/yN2Uzq36GktkxRMYk2C4k2s/JlIkgBjYiI1CmHyuXK+AGvz4/XDyvK7WhKc9gZdaWDu664iL+v2gGUbMW+pWdLXF4f7VKUK1NbKKAREZE642huAW4/rN8ZXCyvlT2O6WF2NJU+fqp/R1o3Tgwk/765cQ8Tb+wYiY8gp6GARkRE6gy3z88xZ2ixvKJiX9gdTVAS1BQWexnx5iYA0h12nh7YSctLtYwCGhERqTM8+MMWy5t1W7cKn1dQ7OXtu3oqV6YWU0AjIiLntbMplncmiTYzbVVbplbTtm0RETlvZec4Wbv9KAdOFJLjdJ+2WN7mvSdIc9jDvkaGI5l6qi1T62mERETkvLQ/t4Cjp1x8kHmQtT8FMW/fdXnYe2ev2820QV0xGgys3XEs0J7hsDN5YEeaaYmp1lNAIyIi54XytWVsViP//G8WXVo2YOhPRxTUiw3/a6/A7WXMgs2sGJ2u2jJRSgGNiIhEvQM5Tty+n6vL+AG/Hwb3bMXMdbsCxxTc+9vWZDiSWZt1LOQ1ft2yAWajQbVlopRyaEREJKodzS3A44cjp4rw+Hz4/CWF8g7nuUiIMbI5+0Tg3sCxBeXyZTIcdp4a0BFrDfddqo5maEREJKoV+/y4fT5eWh28eyndYefJ/h0Y1edipn64HShZWho1v+Sk7Al925NXWExirAUDP/1CNBoi8hnkl9MMjYiIRDUP8MTSLSG7l9Zl5fDk0i1c3b5pUHuB28v01VnkF3mC2g0GaKJ8mailGRoREYk6ZROA68Va2Jx9glF9HHRtUR+Xx4fNYmJTdi6z1+2m2OMP+xoJNjOniooxUFJnRqdkRzcFNCIiElX25Dh59L3MwFbsd+7qybRBXZmzfncg+RdKDpacNqgrTldxyGukO+wk2sxKAD6PaMlJRESixoHcgqBgBqBhfAxz1u8Oe7DknPW7scfHBLXrLKbzk2ZoREQkapxyeYKCGYCCYk+FB0sW+3ysHJuh2jLnOQU0IiJSa5UvlneqXCIvwP7cogpfI6/IQ/dWDauri1JLaMlJRERqpaM5TvzliuXZE6zEWU1B91lMFW+1TtA5THWCAhoREal1jue5KPTD2qxjgYMlD54s4rNdOcwd2j0oqKnoYMnS5F85/2mURUSk1nH7/Ow/WciKzINB+TFpDjujr2zNxBva88DiTKDkYMlZt3XDiCHoSAMl/9YtCmhERKRWKM2X8TRohcvnZ/onWWF3LgE8dWNHZt3WDZfHR4zZyN7jBTzStx0AeUr+rZMU0IiISMSVry2zYnR6hTuXCt3eoLaSs5uKSKlv47LU8MtPcn5TQCMiIhG1P7eAqSu3MjQtlQevb0t+kZeCcgFLeeWv7z9ZxMfbjvBU/w7V2VWpxRTQiIhIRDmLPYy9ug1f/ngcAJfHR8uGcYzq42D2ut1hg5tEm5mbX/k08DijdTJPD+hIipaY6iwFNCIiUqPK15aJt5o4ECYBOP2nowvGLNgcFNRktE4mxmJUsTwJooBGRERqzJ4cJxPeywwKXDJaJzOi98Vszj4RdO+6n+4Zlp4aOKMpw5HM6D4O8KNzmCSIAhoREakR+3ILQoIZgLU7juHz+4MCl1LrsnJ4pG972qfUI8Zs5HBeEX4/WM0qoybB9F+EiIjUiFNFFZ+51LVF/bDX8sscd2AAUpJsXKDlJSlHMzQiIlJtyubLGCo+oQCXxxe2PcFmxo+fRJuFNk0TlSsjYSmgERGRapGd42R91jEa17Ph8vhITY6v8P6YMMtIGY5kbBYj7VJUW0YqpoBGRESq3IHcAg6eLGJF5sFAcu+oPg7SHfbA47IyHMkcySsq12ZnVB8HsSZlR8iZ1fh/Je+++y5t2rQJ+d+7777L559/zvXXX0+PHj2YM2dO0PM+/PBDrrzyStLT01m+fHlNd1tERM6By+tj+uodQcHL7HW7GZqWSnq5gyTTHXYmDeiI2WRk1m3dmDH4Umbd1o3fdW5GsyQbTbXEJGehxmdo+vXrx29/+9vA44KCAgYMGMDFF1/MsGHDuP322+nXrx/3338/7dq1o2fPnmzfvp2//vWvPPHEE3Tu3JnRo0fTvn17LrroopruvoiIhFG+tgwQOMagVIHby5gFmxmWnsojfdtzoqCYBJuJI3kuhsz6jNeHXUaRx8epwmISY5UvI+emxgMaq9WK1WoNPJ4/fz5XX301mzdvpnHjxowcORKDwcCIESNYtGgRPXv25J133qFHjx7cfPPNAAwePJj333+f++67r6a7LyIi5YSrLbPo7svD3lvg9jJ9dRZXXNKIQa9tDLRntE7GYIRLGsfz9ddfc8mFv8JkMlV73+X8EdEcGpfLxeuvv87bb7/Nyy+/TI8ePTD8lAbfuXNnXnjhBQC2bdvGb37zm8DzOnfuzMsvv3zO7+f1Vnw2SHUrff9I90POjcYtOmncasbBPBczPtnBk/074PH6ySsspt5PMzQVSYj5+ddPRutkxl7VGqvRqHGLUtU1bufyehENaJYtW0bnzp1p3rw5+fn5XHzxxYFrCQkJHDlyBACn00nz5s3DXjsXmZmZv7zTVaC29EPOjcYtOmncqle9lq0Z1bs1+/MKiY8x4/OXnHwdbzXRp20jVm87GvKcNIcdAzBj8KXEmI0cyXPRODEGi8dJZmY2oHGLVpEct4gGNG+99RajR48GwGQyBS1FxcTEUFRUdMZr56JTp04RncL0er1kZmZGvB9ybjRu0UnjVn0O5rkC+TJxRhOFXh8vrc4KOc7g0b7tAYKCmjSHndvTUsEAyQnWQG2ZlHoxgI2kpCSNWxSqru9b6euejYgFNHv27CE7O5tevXoBkJSUxPHjxwPXnU4nFovljNfOhclkqhVfkNrSDzk3GrfopHGrWntynDz6XmYg4XfNA715YumWsMcZPLV8C7enpTK4x4W4PD5izEY27z3Bgs/28OQNHWjb9PRnMWncolMkxy1im/tXrlxJ7969A4FJp06d+PrrrwPXv//+e5o0aXLGayIiUjMO5BYEBTMATrf3tMcZrMvKwWYxcce8Lxnx5ibumPclX2fn8li/DliMZygbLHKOIjZDs3btWgYOHBh43KdPHyZNmsSGDRvo3r07M2fOJD09HYBrr72WQYMGMWTIEJo3b84bb7xB//79I9V1EZE66ZTLw1fZJxjVx0HXFvVxeXzkFRZX+JwYs5EPxmYEtmIb8GM2oNoyUuUiEtAUFRXxzTffMGnSpEBbw4YNGT9+PMOHDycuLo7ExESee+45ANq2bcuQIUO46aabiImJ4cILL+SWW26JRNdFROqM8rVlCtweXv1/v8aeaMWPgVNnsaMp/qfdTAZDycGSiTaLastItYhIQGOz2fjuu+9C2gcNGkR6ejq7du2iW7duxMf/fO7Hfffdxw033MDhw4fp3r17UJKwiIhUrfK5MgD/uS+dhvFWHlnyXWCZqaLjDNIddhKtJprbKz7DSaQq1LqznFq0aEGLFi3CXnM4HDgcjhrukYhI3bIvt4CpK7cyNC2VB69vS36Rl0SbmTizmYfLFdCbvW430wZ1BQgKatIddp4e2IkY5cpIDal1AY2IiERWQbGHe69pw5e7S3aXujw+Cou9xFpMbM4+EXxvueMMSnNlEqwmbEAjLS9JDVFAIyIiQfkyTRJjOJRXxPLMg8G1ZRzJTBvUlTELNlPg/rmCa+lxBr9pnVySKAMYjAYFM1KjFNCIiNRx2TlO1mUdo0k9Gy6Pj+SEGKZ/khVaWybrGD78DEtPZfrqrJDXSbRZaJdy+toyItUpYnVoREQk8g7kFnDwZGFQW2Gxl1+1bECcNbRA2vqsHLq2qB/Snu6wk2jTv5ElcvRfn4hIHeby+PD5YUW55aU0hz3s8lI4pQnA2o4tkaSARkSkDilfW8ZqNoZdXip9HG55qXmDWFaOzeBUUTGJNguJNrOCGYk4BTQiInVEdo6TR8rVllk2Ou20Rxesz8phWFpqUFuGIxmr2UhqckK19lXkXCmHRkSkDjiQWxASzAA4iypeTnJ5fIE/ZzjsjOrjwGbSrw6pfTRDIyJSB5xyeUKCGQCzqeLCdxfa45gx+FJizEaOnHLRLMlGipaXpBZSQCMicp4qmy9jNIQPXNZlHSPDkczarGMh1zJaJxNnMZGcYCXRZqFN00TlykitpXlDEZHz0NEcJ36fP/A4wWZmVB9HyFbsV9fs4vb0VmS0Tg5qz3DYGXWlA4vRwGWpdtql1FMwI7WaZmhERM4zR3MLKPDDo0uCc2bSw2zFLnB7+ffGPUy+sSNZR/NxeXwly0t5RTStZ8Oks5gkSiigERE5z7h9/pBgBn4+PLLsVuw0h507My7G5/eTUj82cBZTs/o2TAaULyNRQwGNiEiUK19bxmAgbAIwlAQ1D13flvYp9YgxGzmcV4TRUHIEkwEw/PTnRJtFS0wSVRTQiIhEsb05TvafLCQ+xozPDx6fn6LiirdiF7h+vm4AGiZYcfl8OodJopoCGhGRKHUotwCX189Lq4Mr/S4ZkVbh85JiLfj8kGArSRD++//+wIS+7au1ryLVTQGNiEiUcvv8PPvB93Rt2YBhaam4PD5sFhOxViNpDnvYCsBpDjtWs5FBr20EdA6TnD8U0IiIRJGgfJk4C4N6XMic9buDzluaO7Q7t/90ZEH5AydvT0vlRIGbt+/qqXOY5LyigEZEJEpk5zhZn3WMxvVsuDw+kmItzFm/O2Qmxuv3M2bBZoalpwZmbmLMRjbvPcGYBZt5d0Qv2jZVvoycXxTQiIhEgf25BRw6WYi/TJsf6NqyAZuzTwTqygBs3nuCri3rh5ySDSVLTAkx+tEv5x/9Vy0iEgWKPT78GPgg82CgngyULCOVL5Y3e91upg3qitFgYO2On480UL6MnM8U0IiI1ELla8vEmI1MX70jKJiBn3NkyhbLK3B7GbNgM8tHp+Py+DhVVKx8GTnvKaAREallsnOcPPJecKXfD8ZknLZY3vqsHIb9lARc6tKW9bEYDVyk2jJSRyigERGpRQ7mFjBp2Ra6tGzA0DJbsZ1uT4XPc3l8gT+XLi1ZdQ6T1CEKaEREapFTbg+39LyQ2euCt2LP/0uPCp+XmhzP28N7khhrIc5qwgI00fKS1CHGSHdARKSu25dbwNaDeXy2Kwe/H+as2x2SK7NhVw7pDnvY56c57FhNxpJzDACT0UAze3x1d1ukVtEMjYhIBO3JcfJomXyZD8eGz5Up3blkKLdzKc1hZ9SVrTEZ4LLU8AGPSF2ggEZEJEL25xbwVLl8GZ/fz6g+Dmav2x1UW6Z059K79/Ri/4nCQLG8w3lFmIxgNWvCXeo2BTQiIhGSf5p8mXC1ZaAkqPH6/UGvYTAYaFrPRjPly0gdV+mQftmyZdx33338+c9/5scff2Ts2LEcP368KvsmInJeM5wmX2Z9Vg5z1u9mWHrwVuw0h504i4lm9WNJTrDSrH4sGa2Taal8GZHKBTR///vf+Z//+R+aN2/ODz/8gNFY8jJPPPFElXZOROR8Ujb5d9vBPPxQYW2Zri3qBx6nOeyMvrI1BqBdSj0uS7XTLqWeCuWJ/KRSS05vv/02c+fOpU2bNrz11luYzWbGjx9P3759q7p/IiLnhT05Tia8lxl0kOSs27pV+Jz4GDMzBl9KjNnIkZ9yZSzKlREJq1IBTb169Th06BBt2rQJtB0+fJjk5OQq65iIyPli30/F8rq2bBA4/dpmMVHPVvGP4KRYC8ed7sDjJvVsXKAZGZGwKhXQ3HPPPYwcOZJrrrkGt9vN3Llz+fjjjxk9enRV909EJCoFncUUZ2FQjwuZsz44+feZgR3JcCSzNutYyPPTHHYMQHKClUSbhTZNE7W8JFKBSgU0AwYM4IILLmDp0qVcdtllFBYWMmXKFLp1q3j6VESkLihfW2bF6HTmrN8dtNwEMHnF1pJlJwMhtWVuT0vFaFRtGZGzVelt2927d6d79+5V2RcRkah3ILeAf6z6gQeua8t4k5FThcUYjYaQYAZKtmHfMe9Llo5KY09OQaC2zOa9J3jr82wm9WsfgU8gEp0qFdDs3LmTCy64AJvNVtX9ERGJagUeD2N+ewmPLfkuMEMzY/Clp7/f7eVInos75n0ZaMtwJDN5YEcsOlxS5KxVKqC57bbbeOGFF+jRo+LD0kRE6poYo4kJZZabAGLOsDOpYYKVlWMzOFVYTGKshQSrCRvQSDkzImetUvv/Bg4cyLvvvlvVfRERiTrla8sUFHtDasts3nuCtNMcLJnusBNrMZU8+GlCxmA00EjF8kTOSaVmaHr16sU//vEPhg8fzuDBg4mL+/lfEcqrEZG6IjvHybqsYzSpZ8Pl8eF0e0kMsxW79GBJICiXJt1h58n+JUtL7VLq1Vi/Rc5HlQpoHnnkEQCOHDnCxIkTA+0Gg4GPP/64anomIlKL7c8t4ODJwpD2eKuJOKsp7MGSw9JTebRve04UFJNgM+F0eYgxGXQOk0gVqFRAs3r16qruh4hIrVe2tkzjxBh8fliReTBo1iXDYWfWbd24Y96XIUHN5uxcft/1AkxGMBuNNG8Qp9oyIlXkF5+2ffz4cRo2bFgVfRERqbX2/rS81Pin5aVGCTFM/yQrZDt2Sf6Mgcf6tmf8e5mB9jSHnVFXtsbj86m2jEg1qFRA43a7+Z//+R/efvttXC4XNpuNP//5z9x3331Yrdaq7qOISEQdyi3g6KlCftWyPn4MnCosxuX10bVlAzZnnwiaiQFYm3WM8b9rx6zbugVqyxzOK6Ko2AtYIvMhRM5zldrlNGXKFL755htmz57N2rVrmTVrFps2bWLq1KlV3T8RkYhze3w0TLDxzIqt/O4fa/nTqxu5/h9r2Zydy7RBXYmzmkKe43R5gh4fOFnEm5/tId76iyfGRSSMSn2zPvjgA+bPn09qaioAycnJPPvsswwePJhHH320SjsoIhJpBqOBx8rVloGfdywNS08NOqMJIMFm5uZXPg08znDYmTywkw6XFKkmlQpokpKS2L17dyCgAdi9ezdJSUlV1jERkUgJOlgy1lJy1lKYowugJKgZlpYa1JbhSMZmNpYUyysqJtFmIdFmVgKwSDWqVEBz7733Mm7cOG644QZatGjB3r17WbZsGVOmTKnq/omI1KjsHCfryyT/Ot1emiXZQrZil+Xy+AJ/znDYGdXHgdVsJLVRQk11W6TOq1RAc91119GsWTMWLVrEF198QUpKCm+88QYdO3as6v6JiNSYA7kFHMor4oPMg0EzMhmtk5k2qCtjFmwOG9S0ssczY/ClxJiNHMkrIiXJpqUlkRpW6ey0zp0707lz51/05s8//zw7d+7kX//6FwDbt29n/PjxZGdn84c//IEHH3wQg6GkFvjnn3/OE088wfHjx7n77ru5/fbbf9F7i4iU5/b6ePX/dtKlZQOGpqXi8viwWUxsys5lwWd7wubKpDns2CxGkhOsJNostGmaqKUlkQio1C6n/Px8nn32WT7//HMA7rzzTp588kmcTudZv8a2bduYP39+oOqw2+3m7rvvpkOHDixevJidO3cGzos6fvw499xzD3379mXhwoUsW7aMjRs3VqbrIiJByp7F5PL6+HOPC9mcncsd875kxJubGDb3CzZn5zKox4WkXRxcP6a0tkyxt6S2TLuUegpmRCKkUgHN448/zvfff0/jxo0BuOOOO9i7dy+TJ08+q+f7fD4ef/xxhg4dSosWLQBYs2YN+fn5jB8/npYtW3L//fezaNEiAJYuXUrjxo0ZOXIkrVq1YsSIEYFrIiKVtT/HibtM/gt+mLN+d0ixvPVZOcxZv5sGcVZm3daNGYMvZdZt3ejbKYWiYi/+Gu63iISq1JLT2rVrWbJkCRdccAEAPXv25IILLuAPf/jDWT1/wYIFbN++nT/+8Y98/PHHZGRksG3bNrp06UJsbCwAbdq0YefOnQD88MMP9OjRI7D81LlzZ1544YVz7rfXGz6hr6aUvn+k+yHnRuMWnc40bkfzXHj88MT73wXyZT4YmxESzJRan5UTErgcOFnEx1sPM+nGjvrvo4ro+xadqmvczuX1KhXQNGzYkB07dgQCGoBdu3ZRv379Mz7X6XTy0ksv0aJFCw4cOMD777/PP//5T7p160bz5s0D9xkMBoxGIydPniQ/P5+LL744cC0hIYEjR46cc78zMzPPfFMNqC39kHOjcYtOpxu3Jq3a8uiS4Noy+UWesPeWvX7HvC8Dj9Mddp4e2Amrx8nXX2+tmg4LoO9btIrkuFUqoBk9ejT33Xcf/fv3p0WLFuzfv5/333//rJacVq1aRWFhIfPmzaNhw4Z4PB5uuOEGFi9ezO9///uge2NiYigqKsJkMgUdqVDafq46deqEyRRa0bOmeL1eMjMzI94POTcat+hUftwO5rmCasvkF3tDasuEq/hbVoLNHFJbJqVeDGDTmXZVRN+36FRd41b6umejUgFNv379aNasGUuWLOGzzz4jJSWF2bNn86tf/eqMzz106BBdunQJfPnNZjNt2rRh165dHD9+POhep9OJxWIhKSkp6Fpp+7kymUy14gtSW/oh50bjFp1MJhP7ThTxaLlKv2/fdXnIvYfzikh32FkXZtkp3WEnwWqihT2+WvsrJfR9i06RHLdKb9u+9NJLufTSS8/5eU2bNsXlcgW1HThwgAkTJvDvf/870LZ3717cbjdJSUl06tSJ5cuXB659//33NGnSpLJdF5E65FCei6krtzI0LZUHr29LfpGXRJuZWIsppFjeQ4u/5c2/9OSp5VuCgprSpaUYoyESH0FEzkKldjnl5uby97//Ha/Xy759+xgxYgR33XVXIIm3IldccQVZWVksWLCAQ4cO8frrr7Nt2zauueYa8vPzWbx4MQCvvPIKvXr1wmQy0adPHzZt2sSGDRsoLi5m5syZpKenV6brIlLHFBR7uPfqNsxdv5sbXlrPoNc20u+ldTy5bAuzbusWtMx0LN/N4JkbGdXHwcqxGbw9vCcrx2Yw6caOWIDG2pItUmtVaobmr3/9KxaLBYPBwOTJkwPJwI888ghvvfVWhc9t0KABr776KlOnTuW5556jUaNGvPjii6SkpDB58mTGjRvH1KlTMRqNvPHGG0BJEvL48eMZPnw4cXFxJCYm8txzz1Wm6yJSB5SexeRp0AqbycSEMAdLrt1xDIBH+7ZjwnvfBdrbNE0EDNSzmjhVVAyA1WykmYIZkVqtUgHNpk2bWLFiBR6Ph6+++or169eTm5vLtddee1bP//Wvf83ChQtD2q+66ipWrVrFli1b6NKlCw0aNAhcGzRoEOnp6ezatYtu3boRH691bBEJtSfHGZQv8+G9Gac9WHLtjmM8fH1bZt3WDZfHV3J0wSkXKfVsOL1eLku1h32eiNQ+ld62/c033+B2u2ndujVWq5Xt27djt//yL3+jRo3o3bt32GstWrQIFOITESlvf25BSPLvqTNsxS50e0mpH8upwmISYy00SozhxY9+YNy1bau7uyJShSoV0Nx333088MADWCwW/va3v/Htt98yatQoHn744arun4jIWct3efgxp4Clo9Iwm4ycKiwmIabiH3PxMWau/8fawOPSBGAdYSASXSq9bfvKK6/EbDYTExPDyZMnee+997jooouqun8iIqdVmitTWlvGbILX77iMR5d8F6j4u3JMBmkOe9gKwGkOOwYIqS2jYEYk+lRqlxNAfHw8MTExACQlJQUFM+PGjQupKSMiUpX25Tg5VVQcOI7AD1hMJh4rE8wAFBZ7uD0tlTRH6MGSt6elYjRCu5R6OlxSJMpVug5NRdasWcO4ceOq46VFRDicW4DH72fyiq1Bwcv8v/QIKYq3Zscxvtt3kq4tGzAsLTWQ/Lt57wne+jybSf071HT3RaQaVEtAIyJSndw+P4+Um4kBOFFYHHLvq2t2Mf2WrsxZt5vpq7MC7Rmtk3l6QEdtxxY5TyigEZGoUDZfpl6shc3ZJxjVx0HXFvVxeXzYLCaSE6wh1X8L3F5Gzd/MsPRUJvRtr1wZkfOUAhoRqfXK15Z5566eTBvUlTnry826OOzMuq0bd8z7MiSo+To7lz/9ujntUurVeP9FpPpVS0BjMOi8ExGpGvtzC8KcxWTB6/fzw6FTQfeWBDwGHuvbnvHv/XxCb+lWbJvOYhI5b1VLQOP3+898k4jIWSgo9jD26jZMXLYlKGcm3WHnzb/0ZPDMjRzLdwfa12Yd49F+7Uq2Yv9ULC/OasIKNNISk8h5q9LbtiuydOlSmjZtWh0vLSLnuX25BWw9mMdnu3LYdjCPGJMpJJgBWJeVw1PLtzDlps4hr3GyXHKwyWggxa7jUkTOZ5UKaF577TWKi4N/YHz66af8v//3/wBISUnBaKyWWElEzmN7cpyMX/wt1/9jLX96dSPX/WMt+S5v2KJ4UBLUNK4XE9KeaLNwSeN4TLk/cknjeCX/itQBlVpy+tvf/sagQYOwWCyBNofDwddff11V/RKROuZAbgFPLdtCl5YNGPpTvRib5ecTr08nv8gb9DjdYSfRpv0OInXNOX3rv/jiC6AkR+arr74iLi4u8Hj9+vU6+kBEKi3f7WFwz1YcPFkY1F4v1nKaZ5RIsJkCfy57DpPX663gWSJyvjmngOahhx4CSnYxPfnkk4HdTEajkQsvvJCpU6dWfQ9F5LxVtrZMg3gLNouRFZkHg5aY3hrek3SHPaQCMJQEMPVsFt6+q6dqy4jUcecU0KxevRqAtm3bsmzZMhISEqqlUyJy/svOcbIu6xhN6tlweXw0Soxh+idZIfkyo+Zv4q3hPXly6ZagoCbdYefJ/h2xApel2hGRuq1SC83p6emYzVqjFpHK2Z9bwOG8IspWhXF5fGGTf4/lu/nzqxt5e/jlOIs95Bd5SbCZcLo8xJgMmFVbRkSo5C6nmTNnYrPZqrovIlJHFHt8GPCT2iiexvViaBBnxef3M6qPgzirKeT+Y/lujhe4MRuNmAxgNhq5ICmWGFRbRkRKaJpFRKpd2VyZpFgLsWYj9eNjeGJpcH2ZNIedaYO6MmbB5qCjCwASbWZ8fiid1jEYDTRWMCMiP1FAIyLVau9PuTKNf8qVcbq9XJwczxPvh56WXfp4WHpqyMnYMRYjrezK2xOR8M46oLnqqqtYunQp8fHx9OnT57TnNX388cdV1jkRiW6HcgvYf6KQ5eV2Ln0wNuO0xfLWZ+UwLC018DijdTJjr2qNRcU6RaQCZx3QPPvss8TGxgLw3HPPVVuHROT8Uejx8VKYnUunCisullfPZmHG4EuJMRs5kldE48QYLtDykohU4KwDmssuuyzsn0VEyiqbL5NgM4ediUk4QyXfBJsZP34SbRbaNE1UbRkROSPl0IhIldmX42TfyULiY0oSeAtc4av1Hs4rqrBYXqLVRLuUetXdXRE5j2hRWkSqxOHcAoq8Pl5ancUNL61n0GsbyTvNOUwPLvqWx2/oQLojuCBe6dEFMaotIyLn6KxnaCpKBC5LScEidZPL5+fJctuwN+89QZrDHrLsdCzfzT9WbefZgZ3Id3s5VVhMYqyFBKsJC6otIyLn7qwDmrKJwOvXr2fFihUMGzaMli1bcuDAAWbNmsUVV1xRLZ0UkdrP6fayKfsEo/o46NqiPi6Pj1iLiWvaN+HFj7azetvRwL0ZDjv3XdMGV+kBkmVqyzRVMCMilVCppOAHH3yQ1157jdatWwfaunbtyl133cWECROqtociUiuVL5ZnMsL0W7oya93uoBoyaQ47D1zblgeubcOJAk/g2IKDJwppnBSjXBkRqRKVyqHx+/3s27cvqO3AgQMUF1e8FVNEzg/ZOU7WbD/KgROF5DjdHDhZxHFnMXPW7Q5bLO/5/2wjxmwit8DNkTwX67JymPfpjyRYtS9BRKpGpX6ajBo1irFjx9KrVy+aNWvGkSNHWLduHQ8++GBV909EapkDuQUcPFkY0m4zm1hbQbE8l8fHiDc3ASVLTpMHdlJtGRGpMpUKaG6++WY6derERx99xNGjR7nkkku466676NSpU1X3T0RqgbLLS40TY8LeYzRCnNUUcgZTqXyXh7fv6kmizUKizazaMiJSpSo939u2bVvatm2L2+3GbNa0scj5qnxtGbfPR6LNwopyxxlkOJJPe7AkQD2bmTZNlS8jItWjUjk0+fn5PPbYY/Tq1Ytf/epXbN++nd/85jd89913Vd0/EYmgktoyfl4uU1tm2TcHefqDrSG5MmuzjjF3/W6GpaeGvE5G62QSLaaa6raI1EGVCmgmTJjA/v37ee6554iNjSUxMZGhQ4cyadKkqu6fiESQy+dn4tLvgnJjuraof9qDJddl5dDrouBieRmtk3l6QEcsKpYnItWoUmtFGzZsYNmyZaSkpGA0GjEYDAwcOJCXX365qvsnIjWo/FZsDPBVudoyjRNjGNXHwex1u8MuLVnNRlaOzQgqlmdDxfJEpHpVaobmoosu4t133wXAYDBgMBj44osvgurSiEh0yc5xsrbcVuwCl4dpg7qyOTuXO+Z9yYg3N3HD9PVszs5l2qCuxFlDl5ESSw+eLFMsr5E9vgY/iYjURZWaoXn00UcZPnw4b731Fk6nk3vvvZcDBw7wz3/+s6r7JyI14EBuAUdPFZHaKJ74GDP5RV4SbWbirSZeWLU9bG0ZgGHpqUFF9DJaJ5MQY9Z2bBGpcZUKaDp37syqVav473//y6FDh2jatCm9e/cmMTGxqvsnIjWg2OsjKS4mJF9myci00+bLrM/KYVjazwnAGa2TmTygo4IZEYmISu+3TkxM5IYbbqjKvohIDSqbL5OSZGPi0syQwnjhCuiVVc9mUW0ZEakVKpVD89Zbb3HkyJGq7ouI1JCjOU78Pn/gcYHbG7bKr9VU8Y+IBJuZy1LttEupp2BGRCKqUjM0c+fOJSUlhcaNG1d1f0Skmh3NLaDADxOWZAaWk14fdlnYezfvPUGawx522SndYaeeTUU1RaR2qNQMzYgRI/jnP/+J0+ms6v6ISDVz+fw8UiaYAWgQbwl77+x1u7k9LZUMR3JQe7rDztM6i0lEapFK/fMqOzsbgGuuuYYBAwYQGxsbuDZq1Kiq6ZmIVImQ2jLApnK1ZcxGY9iZmAK3l/mf7eHpgR1xur2cKipWvoyI1EqVCmj2799PamoqqampHD9+vKr7JCJV5ECOk7Kl7/xAobuktsyc9bsDW67nDu3O7T/tWCob1KQ57NzS40IKPV7apegcJhGpvSoV0Dz77LNBj3NycrDb7ae5W0Qi4WhuAcV+eHRJ8O6l1eOuCKkt82V2Lt8fOEnXlg0YlpaKy+Mjxmxk894TvPVZNk/e2CESH0FE5KxVKocmKyuLgQMHsnLlSgCGDh1Kv379+PHHH6uybyLyCxT7/CHBDIDL4wtZWpq9bje39LgwqCLwHfO+5JvsXB67ob2Wl0Sk1qvUDM3jjz9O9+7dSU9PB2DhwoXMmDGDxx9/nNdff71KOygilXOq2MvWQ6eYdVs3GteLCVT/NRogzmoKOoepwO1lzILNDEtPZcLv2uF0eZQrIyJRpVIBzdatW3nxxRcDlYHj4uK49dZb+d3vflelnRORc1M2AbherJm377qcx9//jnVlZmQyHMlMG9SVMQs2hwQ101dn0a9zCpelaglZRKJLpQKaNm3a8N5773HXXXcF2t5//30dTikSQXtynEx47+ft2KP6ONicnRuyvLQ26xh+/CHnMEHJ8QWJMaotIyLRp9JLTnfeeSfvv/8+zZs3Z//+/Zw8eZKZM2dWdf9E5Czsyy3gkfeCa8t0bVE/JGAptS4rhxG9HcEHSzp0FpOIRK9KBTTt27fnP//5D//97385ePAg/fv3p3fv3iQkJJzV8ydPnswbb7wReNyyZUtWrVrF9u3bGT9+PNnZ2fzhD3/gwQcfxGAwAPD555/zxBNPcPz4ce6++25uv/32ynRd5LwQrrbMujDJvxWxmI2sHJuh2jIicl6o9NxyQkIC/fr1q9Rzv/vuO1599VW6du0KgNFoxO12c/fdd5Oens7f//53Jk+ezLvvvstNN93E8ePHueeee7j99tvp168f999/P+3ataNnz56V7b5I1Nqb42Rd1jEa17Ph8vhwur00S7KFJPrGmM9wDlOMWbVlROS8UeOL5R6Phx07dtCtWzfi4+MD7R999BH5+fmMHz+e2NhY7r//fiZOnMhNN93E0qVLady4MSNHjsRgMDBixAgWLVqkgEbqnEO5BRw4WcSKzIPBib6tk5l+S1dGzf850fdM5zAlWk011m8RkepW4wHN9u3b8fl8DBgwgMOHD9O9e3eeeuoptm3bRpcuXQLHKLRp04adO3cC8MMPP9CjR4/A8lPnzp154YUXzvm9vV7vmW+qRqXvH+l+yLmpTeNW6PUxffWOkOWltTuOgR+G/+YiXvxoB1BSW2baoK4YCF6OKj2HyWI01IrPVF1q07jJ2dO4RafqGrdzeb0aD2iysrJITU3lscceo0GDBjz77LM89thjtG7dmubNmwfuMxgMGI1GTp48SX5+PhdffHHgWkJCAkeOHDnn987MzKySz/BL1ZZ+yLmpDeMW16x1SKG8UmuzjjHumjaBgKb0HKYHr2vLeKMBZ5GHxFgLcVYTFsDocfL111trsPeRURvGTc6dxi06RXLcajyg6d+/P/379w88fuKJJ7jqqqu4+OKLsVqtQffGxMRQVFSEyWQKulbafq46deqEyRS5aXav10tmZmbE+yHnJtLjdjDPFUgAzi/yVHiv2+tl1m3dgo4u+POrG3lvRC8omeDEZDTQpF4MYKNhw4bV/wEiJNLjJpWjcYtO1TVupa97NiJecMJut+Pz+UhOTmbHjh1B15xOJxaLhaSkpKBDMEvbz5XJZKoVX5Da0g85N5EYt+yfEoCb/JQA3LJhHKP6OJi9bndQAnApm8XEH1/ZGNSW0TqZhBgzbZrWzQRgfd+ik8YtOkVy3Go8oJkyZQrt27fnhhtuAGDz5s0YjUbatGnDO++8E7hv7969uN1ukpKS6NSpE8uXLw9c+/7772nSpElNd12kRu3PLWD/iUJWZB4MSuxNd9jDVvpNc9gxGw1Br6HaMiJSV9R4QNO2bVtefPFFkpOT8Xq9PPXUUwwYMIC0tDTy8/NZvHgxN910E6+88gq9evXCZDLRp08fJk2axIYNG+jevTszZ84MnCMlcr5yeXxM/yQrZJdSaYJv2Uq/aQ47o65sTZzFpNoyIlIn1XhAc+ONN5KVlcXo0aMxmUzccMMN3H///ZjNZiZPnsy4ceOYOnUqRqMxUHyvYcOGjB8/nuHDhxMXF0diYiLPPfdcTXddpFqVL5bn9fnDbrmGkqDmkb7taZ9SjxizkcN5RRQVe3H5fKotIyJ1UkRyaMaNG8e4ceNC2q+66ipWrVrFli1b6NKlCw0aNAhcGzRoEOnp6ezatSukho1ItCt/DhPA23ddXuFzyiYIHzhZxOqtR3j8hvbV1kcRkdos4knB5TVq1IjevXuHvdaiRQtatGhRsx0SqWb7cguYuGwLXVs2YFhaKi6PD5vFRMIZDolMjDVz8yufApDhsPNE/45Yy+XQiIjUFbUuoBGpa/JdHoZc3opZa3cFHRb5t5s7k9E6uaRoXjkZrZOJMRtZcGdPEmwmnC4vNpOBpsqXEZE6SgGNSASUz5c5eKKQr7JPBN3z6PtbmHv7ZQBBQU1G62TGXNUagx9MRjAbjTRvYFXyr4jUaQpoRGpYdo6TR97LDKr4m+Gwh5zFVOD2MnTO56wYnc6uY85AsbzDeUX4fX4sRgOXpdoj9TFERGqVio/jFZEqdSC3ICSYAViblcOcdT8y/DcXBbUXuL0cy3cHtRmApkk25cuIiJShgEakBp1yeSo8iyndkRzSHhdjoln9WJLjrTSrH0u6I5lYoJGWmEREArTkJFKNyufKnDrDWUwenz/ocZrDTqzFhMvjC5zFZDAaFMyIiJSjgEakmuzJcfJoueWlFaMrrnAdZ/35DJTS6r9Wo4GLVCxPRKRCCmhEqsH+3IKQYAYg1moizWEPWwE4zWEnzmpixuBLiTEbOZJXxAX1bViUKyMickYKaESqwelyZXz4GXWlAyAoqElz2Bl9ZWvMBgPJ8VYSYy2kJsdjAppoeUlE5IwU0IhUkbL5MsbTzKqMeGMTrw3pRr9OKYGqwKWzMc3q23D5PIFcGavZqFOyRUTOknY5iVSBAzlO3B5f4PHpji3YfiSfO1//knRHctDOpcsvtjNq/ia8npLaMu1S6qlQnojIOdAMjcgvdDS3AI8fjpwqIj7GjM9fMsnyzMCOTF6xNVAor1SjejHkuTz0nbYuqD3dYSfRpq+kiEhlaIZG5Bdy+/wU+33sOurkSJ6L3AI3B04WYcDAnKHdQnYu3Z6WGvLFS3fYeXpgJy0xiYhUkv45KHKOyteWSbCaOJLnYkXmwTCJvg7evqsn2ccLiTEb2bz3BPM/28OD17Vl5dgMThUVk2izkGgza4lJROQXUEAjcg7C1Zb5YEw60z/JCtmKXfr4oevaMuLNTcDPMzQA7VRbRkSkyiigETlLh/JcPLVsC11aNmDoTzuUbBYTPj9h68pASbvJYAjUltm89wQLPtvDxBs71nDvRUTObwpoRM6S0+3h1stbceBEYVC72WggzmoKSf4tdcrlCczQlObKaHlJRKRqKaARqUBpvoynQSssRiMxZiPLy+XKZLROZtqgroxZsDlsUFM/zsLbd/VUroyISDVSQCNyGvtynJxye/D/VOnOD7wUJldm7Y5j+P1+hqWnMn11VtC1jNbJJFpMtG2qfBkRkeqkbdsiYRzOLcDn93OysBivz4fPDy6Pj64tGwRtwy61LiuHXhfZg9oyHMlMHtARq85iEhGpdpqhEQnD4/NT7Pez+6iTxvVsuDw+Cou9NEuy8fItlzJy/qaQ5SWr2ViyFbuwmMSftnPbgEZaYhIRqXYKaEQIrS0TbzVx7JSbFZkHWVc2X8ZhZ1Sf1tx1xUX8fdWOoNdIsJnx+wmcxWQwGhTMiIjUEAU0UuftzXGy/2Rh4NgCj8+Px+9n+uodQcEM8FP9GQMPXd82KKDJcJTkylxgj6/h3ouICCigkTruQG4BLq+fl1YHJ/uuHJsRVDyvrLVZx5hQJi8mw2Fn8kDlyoiIRJICGqnTPD4/r67J4sn+HfB4/eQVFlMv1kJ+kafC5+UXeXh7eM9ArowF5cqIiESSAhqpc8rmyzRMtDCid2seWZIZNEOzYkx6ha8RF2PC6SoJegxGA00VzIiIRJQCGqlTyufL2IwmHn4vM6S2jM1sIs1hD3ukQZrDTqzFRIdmSTXVbREROQPVoZE640BuAcU+f1BbvtsbNmjZe7yAUVc6SHME15ZJc9gZdWVrYsz66oiI1CaaoZE6w1cumAHIKywOe+9fF33D/Dt70q9TCsN+OogyxmzkSF4RF9S3cYGWmEREahUFNHLeKl9bJsFqwucP3tH0wdiMsM89lu/mltc2svieXhS4vYFieW2aJuosJhGRWkjz5nJe2p/jxO3xBR77f/rfE0u3BC0xebw+0sstK5Vq2zQRE3BJ43hMuT9ySeN4BTMiIrWUAho57xzJLcDvB5fHS9lFJo/Pz+bsE0H3jnxzE08N6BgS1KQ77Dw9sBMxqi0jIhIVtOQk5x2fz48HP5NXbA2ajclwJDNtUFfGLNgcOIdpb24hQ2Z9zsuDL+URkzHsOUxer/c07yQiIrWFZmjkvLAvt4CtB/P4bFcOxcAjS74L2b20NusYc9bvZlh6alD73txC+k9fX/Kg7DlMOsZARCRqKKCRqHc0x4m/zA6mfLeXYWmpJCdYQ+5dn5VD1xb1Q9rTHXYSrSYuS7XTLqWecmVERKKMAhqJakdzCyj0l8y+HDhRSI7TzcGTRRzJK2L+nT3DBjXlKV9GRCT6KYdGoprb52f/yUJWZB4MWmJKc9hplRzP//yhC0PnfhH0nAsaxLJybEZQvowZncUkIhLNFNBIVClfW8ZqNvLa2l10bdkgUADPZjGxKTuX19buYsLv2gU9P91hJ95iIt/tDcqXSVEwIyIS1RTQSNTYk+NkQrlzl1aOSeeWHhcyZ/1upq/OCrSnOezcnpZKcZlaNOkOO5MHdMIItEupV5NdFxGRaqaARqLC/twCHglziKTBYGDO+t0h7aWPH+vbnoXDe5IYa8GAH7/fj9mk1DERkfONfrJLVDhV5GFdmEMk/RD2cEl+avcDPj94fT5OFnqwGg000fKSiMh5RzM0EhXyXZ6w7c7TtJe9bjKC2WikeQOrDpUUETlPKaCRWqtsAnC9WEvYe+JjKv5POMFmpm1T5cuIiJzvtOQktdLeHCf7cgvw+HyU1sx7a3iPkLoyh04WkuFIDvsaGY5kEs4Q8IiIyPlBP+2l1jmQW4DL6+Ol1VlB+THpDjtvDe/Jn1/dyLF8NwBvf7mXSQM68PiS71gbdG6TnckDO6rir4hIHaGARiKufG2ZWKuJJ5duCUn2XZeVw5NLt7BgeE92HM4nxmxk894T/OOj7TwzsBP5bi+niopJtFlItJkVzIiI1CEKaCSiDuc48Zd57AcK3N6wO5qgJKhxFfsY8eYmoKTezKgrW+NHtWVEROoyBTQSMUdzC3D7Ib/Yg5+fz1EyGiDOaqLA7Q37vKJiLzMGX0qM2ciRvCIa14shfMqwiIjUFQpoJGKKfX68wOQVW4OWlzIcyUwb1JUxCzaHDWriY8w08PpJsJmIjzFhNRow62BJEZE6LeK7nO644w7effddAD7//HOuv/56evTowZw5c4Lu+/DDD7nyyitJT09n+fLlkeiqVIF9uQVsPZjHZ7ty8AKPLAmt/rs26xhz1+9mWHpqyPPTHHYMgMlQUlvmgqRYYtDBkiIidV1EA5qlS5eybt06AI4fP84999xD3759WbhwIcuWLWPjxo0AbN++nb/+9a+MGDGCWbNmMW3aNHbt2hXJrkslZOc4WbP9KAdOFJLjdJPv9p62yu+6rBx6XWQPais9nwkDQQdLNrbHV3PPRUSktovYktOJEyeYMmUKqakl/wpfunQpjRs3ZuTIkRgMBkaMGMGiRYvo2bMn77zzDj169ODmm28GYPDgwbz//vvcd999keq+nKMDuQUcziui7MJQXmFxhc8xm4zMuq0bLo8vsKNpwWd7mHhjRxXLExGRIBELaKZMmcJvf/tbXC4XAD/88AM9evTAYCj5lde5c2deeOEFALZt28ZvfvObwHM7d+7Myy+/fM7v6fWGTzKtKaXvH+l+RILb48Pr87M882BgVmbl2IwKnxNvNfHHVz4NPE532Hl6YCdS6sXU6N9hXR63aKZxi04at+hUXeN2Lq8XkYBm48aNfPrppyxfvpzJkycDkJ+fz8UXXxy4JyEhgSNHjgDgdDpp3rx52GvnIjMz8xf2vGrUln7UpAYt2/Dq2l10bdmAYWmpuDw+4iwm0h32sFu00x126tnMrBybwanCYhJjLSRYTdh8BXz99bYIfIK6OW7nA41bdNK4RadIjluNBzQul4snnniCJ598koSEhEC7yWTCav25rH1MTAxFRUVnvHYuOnXqhMlk+gW9/2W8Xi+ZmZkR70dNOJjnCiqWZwBu7dmKmet2MX11FgDJCVbeGt6TJ5duCQpq0h12nuzfESNwSePy+TE2GjZsWGOfA+rWuJ1PNG7RSeMWnapr3Epf92zUeEAzY8YMOnbsSO/evYPak5KSOH78eOCx0+nEYrGc8dq5MJlMteILUlv6UV325DiZ8F7w7qWM1smM6H0xm7NPBNqO5bv586sbmX5LV8b/rh15hR4SbCacLg9WowGj0VCr/p7O93E7X2ncopPGLTpFctxqPKBZtmwZubm5dOvWDYCioiJWrlwJQNeuXQP3ff/99zRp0gQomVn5+uuvA0nBZa9J7bIvtyAkmAFYu+MYPr+fYempgRkaKA1qPmPl2AyMBjAZjSTFWjAaIEVbsUVE5CzVeEAzf/58PB5P4PHUqVPp0qULAwcOpHfv3mzYsIHu3bszc+ZM0tPTAbj22msZNGgQQ4YMoXnz5rzxxhv079+/prsup1H2LKZ6sZbTbsVen5XDsLTQ2jIATpcHg6FkN3aizaJzmERE5JzUeEDTtGnToMdxcXE0aNCAhg0bMn78eIYPH05cXByJiYk899xzALRt25YhQ4Zw0003ERMTw4UXXsgtt9xS012XMA7mOCmbg55f5DntvQBury9se0KMmbY6i0lERCop4kcflAYtAIMGDSI9PZ1du3bRrVs34uN/Tgi97777uOGGGzh8+DDdu3cPShKWyCg9i2n9zmM0qWfD5fHR6gxF7holxIS0pTvsJFq1Vi4iIpUX8YCmvBYtWtCiRYuw1xwOBw6Ho4Z7JKfj8vk5lFcY1BZjNpLhsLM2zLJTmsNOXLnApbS2jFVnMYmIyC9Q6wIaiR7FPj8+P6woUywvzmpi1m3dAANrs44F7i09tqCw2BtaWwadxSQiIr+MAho5a2WTf5NiLcRaTUz/JCsoCbjA7eWOeV/yaN92jP1ta46ccgWOLRizYDOL7+lVcmOZs5gUzIiIyC+lgEbOyp4cJ4++lxm0lDT/Lz3C7mgqcHuZ8N53zLqtGyPe3BRoT3fYSbSZtYNJRESqXERP25bosD+3ICSYAThxhsMlXZ6fdzSV5soomBERkeqgGRoJK2h5Kc4SNsk3xlxxPJyaHM/bd/Uk0WbRzIyIiFQrBTQS4miOE3+Zx6cKw9eW2bz3BGkOe9hlpzSHHaMBLku1V1MvRUREfqaARoIczS2gwA+PLvl5iWnl2Iyw985et5tpg7piPM2OJoN2YouISA1RQCNB3D4/s9bu5KkBHSks9pFXWIzRAM8M7MjkFVspcP9cF7jA7WXBZ3sYmtaKoWmtcHl8gR1Nb32ezVP9O0Twk4iISF2igEaCuHxehmVczCNLyp2W7Uhm1m3duGPel4GgJs1h56Hr2zH1w218tPXIz/e2TubpAR11uKSIiNQYBTR1XPnaMvFWEw+HOy076xjg563hPdmXWxiYibnltY28c9fljLumDaeKipUALCIiEaGApg7bm+Nk/8lC4mPM+Pzg8fnJd3tPe1r22qwchp5yhdSWsZqNpDZKqKlui4iIhFBAU0cdyC3A5fXx0urgSr9v3dmzwueFqy1zgWZjREQkwhTQ1FHFPj/PfrCVri0bMCwtFZfHh81iol6spcLnqbaMiIjURgpo6pDy+TJjf3sJL360nemrswL3PDuwIxmO5KBt2KXSHXZsZiPtVFtGRERqGQU0dUS4s5jSHXaGpqWycdfxwM6lp1ZsZfZt3QF/yL2P39ABj99X/qVFREQiTgFNHbDvNGcxrcvKwQ8MS08NzNIUuL0Mm/cFy0el4yz2kF/kJcFm4kiei398tJ1HftcuAp9ARESkYgpo6oB8lyfsWUwA67NyuCP9oqC2AreXQ3lF3DLzs0BbaW2ZZsqZERGRWkgBzXmofK5Mviv8WUylzKbQMwoaJlhZOTZDtWVERCQqKKA5z+zLcXLK7cFPSZDiB+JjKh7mBnHWoMfpDjvxFhMt7PHV1U0REZEqpYDmPHIotwCPH55ZsTVoiemDMelnPBW7VGltGZtRJ0uKiEj0UEBzHin2+XlsSWjyrx8/t6elAgQFNaWnYpuMBt4e3pPEWAsJVhMxQCMtL4mISBRRQBPlyubL1Iu1hE3+dXv8zP9sT1ARvdKzmOZ/toeHrmvLTytUGIwGGiuYERGRKKOAJoqVP4sJYNZt3Xho8bccy3cH7jtZUMwtPS5kzvrdQUX0SmdoDAa4TMXyREQkiimgiVKlZzG9vDoraFYmw2Fn/p09ueW1jYGg5svsXL4/cDLsDM1bn2czsX+HSH0MERGRKqGAJkp5fH4mLt0SssS0NiuHp5Z9z/N/6Mztc78EYPa63cy6rRsz/rszaIYmw5HM5IEddbikiIhEPQU0UaJ8bRkMnLZY3tqsY4wvU9G3a8v6GA0Gnh7QEafbq9oyIiJy3lFAEwWyc5yszzpG43o2XB4fTreXZkk24qymwBlM5TldHmYMvpQYs5HDeUUAmIB2KfVqsOciIiI1QwFNLXcgt4ADJwpZnnkwaMt1Rutkpg3qypgFm8MGNXExJsgv+bMBSEmyYVVtGREROU8poKnlXB4fL32SFVIUb+2OY/j9/qCDJUulOezEWUwkx1tJjLVwSeMEbKi2jIiInL+Mke6AhNqXW8DWg3l8tiuHIo8vbIVfKDkt+/KLgrdbpznsjL6yNR6fL6i2TCMdYyAiIucxzdDUMntynEx4LzMQxPzz/11a4f0GSmrPlG7FPpJXhMvjxYdFtWVERKTO0AxNLbIvtyAomAFolBBT4XPqlztYcv/JIt74dA8JVsWqIiJSd+i3Xi1yqsgTsrxkNRsrPFjSgJ875n0ZaMtw2Jk8sJNqy4iISJ2igCaCyteWySssDrnn4MmiCg+WLCz2snJshmrLiIhInaaAJkL25zhxlx7ABPiBxokxIbVlzEYDoxdsZlh6asixBWMWbObdEb1o21S1ZUREpG5TQBMBx/JcFPvhsfe/C64t47Az+7buDJv3RSCo2bz3BF1b1g/Zmg0ltWgSYzSEIiIiSgqOgGKfn0eXZIbWlsnK4eVPsnis78/HFsxet5tRVzrIaJ0cdG+Gw87kATqHSUREBDRDU2NK82U8DVpxyu1lU/YJRvVx0LVFfVweHzaLiU3Zucxet5tH+rZj2eg08ou8JNhM5Jxy0/Oihkz4XTvlyoiIiIShgKYGlK8ts/juy5k2qCtz1u8OWkpKc9iZNqgrTpeHm/71adBrpDvs3Ni5mc5iEhERCUMBTTXbl1vApGVb6NqyQSCpt0G8lb99tD1kyan08VM3dgxqT3fYeXpgJ2J0FpOIiEhYCmiqmdPlYWivVCxmA/ExZvKLvLgqOM5gfVYOLo+vZCt2YTGJsRYSrCas6CwmERGR01FAU8XK15YxmyClfixPLP15R9Nbw3tW+Br5Lg8JMeags5iaKJgRERE5LQU0VWhvjpP9JwuJjzHj84PH5yfOamZ8ueMMzGdYOkq0mVVbRkRE5Bxo23YVOZRbQHGZQnmlnG5vyPLSuqxjZDiSQ+4F1ZYRERGpDAU0VcTj81NS7zeYzWwkOSH4AMlX1+zi9vRWZDiCT8NWbRkREZHK0VTAL1A2X6ZBvAWrycRLq7OCZmTSHXbe/EtPBs/cyLF8NwAFbi+j5pccZzChb3vVlhEREfmFFNBUUvnaMrNu68bs9bvDLC/l8NTyLUy5qXPQqdgFbi9fZ+fyx183V20ZERGRX0hLTpWwL7cgKJgBaFwv5rRbsddl5dC0ni2oLd1h58n+HbGqtoyIiMgvFrEZmry8PHbv3k2rVq1ISkqKVDcq5VSRJyR4yS/ynubuEk6XJ+g4A6fLS4zJQFMtMYmIiPxiEZmhWblyJX369OHRRx/liiuuYOXKlQBs376dm266ie7duzNlyhT8/p+TbD///HOuv/56evTowZw5cyLR7YC8wuKQtgSbqcLnJMZaMBuNmAxgNhpp3iCWFvb46uqiiIhInVLjAc2pU6eYOHEi//73v1m2bBmPP/44zz//PG63m7vvvpsOHTqwePFidu7cybvvvgvA8ePHueeee+jbty8LFy5k2bJlbNy4saa7HlAv1hLSdiTPRXq5XUul0h12Eq0mLmkcjyn3Ry5pHK/kXxERkSpU4wFNfn4+EyZMoG3btgC0b9+e3Nxc1qxZQ35+PuPHj6dly5bcf//9LFq0CIClS5fSuHFjRo4cSatWrRgxYkTgWiQkWE0hwctDi7/lsX4dQtpLz2GyKFdGRESk2tR4Dk1KSgr9+/cHoLi4mHnz5nH11Vezbds2unTpQmxsLABt2rRh586dAPzwww/06NEDg6EkKOjcuTMvvPDCOb+311txnsvZyskv4qkBHXlsyXes+ymX5li+m3989APPDuxEvtsbOIcpxmzEDCTXiwm8f1X1Q2qGxi06adyik8YtOlXXuJ3L60UsKXjbtm3cdtttWCwWPvjgA2bMmEHz5s0D1w0GA0ajkZMnT5Kfn8/FF18cuJaQkMCRI0fO+T0zMzOrpO9xzVozZNbnvDz4UiaYjIHgxeP1ccvMz3h1SLfAvRajAauvgK+/3lbl/ZCapXGLThq36KRxi06RHLeIBTRt2rRh1qxZPPvsszz66KO0aNECqzW4om5MTAxFRUWYTKaga6Xt56pTp06YTBUn756NAyeKuNAeR//p60OupTvsJPyUL/MzGw0bNsTr9ZKZmVll/ZCaoXGLThq36KRxi07VNW6lr3s2IhbQGAwGOnbsyHPPPcfVV1/N/fffz44dO4LucTqdWCwWkpKSOH78eEj7uTKZTFXyFx1jNDB5QCceXZIZWHKCn/NlYoyGCt+nqvohNUvjFp00btFJ4xadIjluNR7QfP7553zyySc89NBDAFitVgwGAxdffDHvvPNO4L69e/fidrtJSkqiU6dOLF++PHDt+++/p0mTJjXd9YDGDeI4mOMMyZdJsJow/3RdREREak6N73Jq1aoVb7/9NgsXLuTgwYP87W9/Iy0tjSuuuIL8/HwWL14MwCuvvEKvXr0wmUz06dOHTZs2sWHDBoqLi5k5cybp6ek13fUgKfZ4DKU7l0r/z2ggRbVlREREalyNz9A0btyYadOm8cwzzzBlyhTS09OZOnUqZrOZyZMnM27cOKZOnYrRaOSNN94AoGHDhowfP57hw4cTFxdHYmIizz33XE13PYRqyYiIiNQOEcmhSUtLY8WKFSHtV111FatWrWLLli106dKFBg0aBK4NGjSI9PR0du3aRbdu3YiP10yIiIiIlKh1p203atSI3r17h73WokULWrRoUbMdEhERkVpPp22LiIhI1FNAIyIiIlFPAY2IiIhEPQU0IiIiEvUU0IiIiEjUU0AjIiIiUU8BjYiIiEQ9BTQiIiIS9WpdYb3q4Pf7gZJjyCOp9P0j3Q85Nxq36KRxi04at+hUXeNW+nqlv8crYvCfzV1Rzu12k5mZGeluiIiISCV06tQJq9Va4T11IqDx+Xx4PB6MRiMGgyHS3REREZGz4Pf78fl8mM1mjMaKs2TqREAjIiIi5zclBYuIiEjUU0AjIiIiUU8BjYiIiEQ9BTQiIiIS9RTQiIiISNRTQCMiIiJRTwGNiIiIRD0FNNXko48+4qqrrqJ9+/bceOON7Ny5E4Dt27dz00030b17d6ZMmXJW5ZwlMu644w7effddAD7//HOuv/56evTowZw5cyLcMzmd559/nrvvvjvwWN+32u2dd97hiiuuoEuXLtx6663s3bsX0LjVRsePH6dPnz7s27cv0FbROEXiZ6YCmmqQnZ3NhAkTGDduHGvWrKFVq1Y88sgjuN1u7r77bjp06MDixYvZuXNn4Bem1C5Lly5l3bp1QMkX+Z577qFv374sXLiQZcuWsXHjxgj3UMrbtm0b8+fP55FHHgHQ962Wy87O5uWXX2bGjBmsXLmSFi1a8PDDD2vcaqHjx49z9913s3///kBbReMUsZ+Zfqlyq1ev9r/11luBx59++qm/c+fO/lWrVvm7d+/uLygo8Pv9fv/WrVv9f/7znyPVTTmN3Nxcf69evfzXXnutf/Hixf45c+b4r7vuOr/P5/P7/X7/qlWr/OPGjYtwL6Usr9frv/nmm/0vvvhioE3ft9pt5cqV/jFjxgQef/nll/60tDSNWy102223+efNm+e/5JJL/Hv37vX7/RV/vyL1M1MzNNXgyiuv5E9/+lPg8e7du7nwwgvZtm0bXbp0ITY2FoA2bdoElqKk9pgyZQq//e1v+dWvfgXADz/8QI8ePQLngHXu3JktW7ZEsIdS3oIFC9i+fTsXXHABH3/8MW63W9+3Ws7hcLBx40a2bt3KqVOnmD9/PmlpaRq3Wuipp55iyJAhQW0VjVOkfmYqoKlmbrebOXPm8Oc//5n8/HyaN28euGYwGDAajZw8eTKCPZSyNm7cyKeffsoDDzwQaCs/bgkJCRw5ciQS3ZMwnE4nL730Ei1atODAgQPMnTuXW265Rd+3Ws7hcHDttdcyYMAAunXrxtdff81DDz2kcauFWrRoEdJW0ThF6memAppq9tJLLxEbG8vNN9+MyWQKOf48JiaGoqKiCPVOynK5XDzxxBM8+eSTJCQkBNrLj5vGrHZZtWoVhYWFzJs3jzFjxjBnzhycTieLFy/W960W+/bbb/nkk094++23+fLLL+nXrx933nmnfk5GiYrGKVI/MxXQVKNPP/2UN998kxdeeAGLxUJSUhLHjx8PusfpdGKxWCLUQylrxowZdOzYkd69ewe1lx83jVntcujQIbp06ULDhg0BMJvNtGnThry8PH3farHly5fTt29funTpQmJiIvfeey979+7Vz8koUdE4Repnprna36GO2rt3L+PGjePxxx/H4XAA0KlTJ955552ge9xuN0lJSZHqppSxbNkycnNz6datGwBFRUWsXLkSgK5duwbu+/7772nSpElE+iihmjZtisvlCmo7cOAAEyZM4N///negTd+32sXn85Gbmxt47HQ6KSwsxGw28/XXXwfaNW61U0W/zzp16sTy5csD12rqZ6ZmaKpBUVERd999N1dddRVXX301TqcTp9NJt27dyM/PZ/HixQC88sor9OrVC5PJFOEeC8D8+fNZtmwZS5YsYcmSJfTp04cxY8bwySefsGnTJjZs2EBxcTEzZ84kPT090t2Vn1xxxRVkZWWxYMECDh06xOuvv862bdu45ppr9H2rxbp168aqVauYO3cuy5YtY8SIETRq1Ihbb71V4xYFunfvftpx6tOnT0R+Zhr8flUsqmofffQRI0eODGn/+OOP+eGHHxg3bhwxMTEYjUbeeOONwAyO1C4PP/wwl112Gb///e9ZsGABTz/9NHFxcSQmJrJw4UKSk5Mj3UX5yVdffcXUqVPZtm0bjRo1YsKECfTp04ePP/5Y37dayu/3M2PGDBYtWsTRo0dp3bo1Tz/9NO3bt9e41VJt2rTh448/DiT8VjROkfiZqYAmAo4ePcqWLVvo0qULDRo0iHR35Czt3buXXbt20a1bN+Lj4yPdHTlL+r5FJ41bdKhonGr6Z6YCGhEREYl6yqERERGRqKeARkRERKKeAhoRERGJegpoREREJOopoBEREZGop4BGREREop4CGhEREYl6CmhEREQk6imgEZFa7auvvmLAgAF06dKFP/zhD2RlZQGwbds2+vXrx2WXXcYzzzzDddddxxtvvAHAmjVruOGGG+jWrRuPPPIIbrc7kh9BRGqAAhoRqbV8Ph9jx47l6quv5qOPPqJ79+5MmTIFgCeffJK+ffvy+uuvs2jRIp5++mn69+/Pnj17GDFiBEOGDGHRokV8++23zJw5M8KfRESqmwIaEanV3nvvPe68804OHjxIXl4eu3fvBmDr1q1cd911tG3bltatW3Pw4EGSkpL44IMPaNeuHTfffDOtWrXilltuYfXq1RH+FCJS3cyR7oCIyOkYjUbmzp3LokWLaNGiBSkpKfh8PgBatmzJ119/TYMGDfjxxx8Dp/weOnSIrVu30q1bNwC8Xi9xcXER+wwiUjMU0IhIrfXZZ5+xaNEiPvjgA+x2O//3f//Hli1b8Pv9OBwOJk+ezGOPPcbgwYNp27YtAE2bNuXKK6/koYceAkqWrQoLCyP5MUSkBiigEZFay+l0ApCXl8ePP/7Is88+i9/vZ9euXXz55ZcsWLCAxMREUlJSAs8pzavZs2cPrVu35rXXXuOrr77i3XffjdTHEJEaYPD7/f5Id0JEJJzi4mIefvhhVq9eTfPmzRk4cCAvvPAC//d//8df/vIXDh48yKlTpzCbzQwYMIBJkyYBJbucnn/+efbu3Uvnzp2ZNGkSrVq1iuyHEZFqpYBGRKLOO++8w4cffsjkyZOx2Wz88MMPDB8+nA0bNpCQkBDp7olIBGjJSUSiTs+ePVm+fDn9+vXD5XLRrFkzxo0bp2BGpA7TDI2IiIhEPdWhERERkaingEZERESingIaERERiXoKaERERCTqKaARERGRqKeARkRERKKeAhoRERGJegpoREREJOopoBEREZGo9/8BnFYiRjxqWMQAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:45.386485Z",
     "start_time": "2025-04-20T03:53:45.238864Z"
    }
   },
   "cell_type": "code",
   "source": [
    "features = data[['income', 'credit_score', 'price', 'items']].copy()\n",
    "# 标准化\n",
    "scaler = MinMaxScaler()\n",
    "scaled = scaler.fit_transform(features)\n",
    "\n",
    "# 添加得分列（简单平均得分）\n",
    "data['value_score'] = scaled.mean(axis=1)\n",
    "\n",
    "# 找出高价值用户（前1%）\n",
    "threshold = data['value_score'].quantile(0.99)\n",
    "# sns.boxplot(x='gender', y='value_score', data=data)\n",
    "high_value_users = data[data['value_score'] >= threshold].sort_values('value_score', ascending=False)\n",
    "data.drop(['value_score'], axis=1, inplace=True)\n",
    "high_value_users.head(10)"
   ],
   "id": "2bbaa5bd83c43a19",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         user_name chinese_name  age     income gender  credit_score   price  \\\n",
       "620564     davxxvn          沈梓涵   98   991000.0      女           837  9891.1   \n",
       "321975   gipxoqmqp          史梓涵  100   985000.0      男           849  9673.3   \n",
       "313711     wkhcykq          卢梓涵  100  1000000.0      女           846  9574.3   \n",
       "245860   tzfoaatfo          侯梓涵   98  1000000.0      女           831  9831.7   \n",
       "259690  gvdguzxyzz          漕欣怡  100   959000.0      男           850  9881.2   \n",
       "162800   BWJMPYNRR           陆明  100   982000.0      女           850  9594.1   \n",
       "547561  yysnxfzutz          易梓涵  100   985000.0      女           846  9544.6   \n",
       "193264     FNDRNBE           贾洋   96   975000.0      男           823  9930.7   \n",
       "198511  LZGXHMMXZD           文勇   99   966000.0      男           838  9732.7   \n",
       "364934  tlmjhlifao          乔欣怡  100   955000.0      男           844  9653.5   \n",
       "\n",
       "        items  value_score  \n",
       "620564     10     0.989116  \n",
       "321975     10     0.987620  \n",
       "313711     10     0.987529  \n",
       "245860     10     0.987152  \n",
       "259690     10     0.986777  \n",
       "162800     10     0.985342  \n",
       "547561     10     0.983035  \n",
       "193264     10     0.979743  \n",
       "198511     10     0.979356  \n",
       "364934     10     0.977352  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>chinese_name</th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>gender</th>\n",
       "      <th>credit_score</th>\n",
       "      <th>price</th>\n",
       "      <th>items</th>\n",
       "      <th>value_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>620564</th>\n",
       "      <td>davxxvn</td>\n",
       "      <td>沈梓涵</td>\n",
       "      <td>98</td>\n",
       "      <td>991000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>837</td>\n",
       "      <td>9891.1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.989116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321975</th>\n",
       "      <td>gipxoqmqp</td>\n",
       "      <td>史梓涵</td>\n",
       "      <td>100</td>\n",
       "      <td>985000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>849</td>\n",
       "      <td>9673.3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.987620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313711</th>\n",
       "      <td>wkhcykq</td>\n",
       "      <td>卢梓涵</td>\n",
       "      <td>100</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>846</td>\n",
       "      <td>9574.3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.987529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245860</th>\n",
       "      <td>tzfoaatfo</td>\n",
       "      <td>侯梓涵</td>\n",
       "      <td>98</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>831</td>\n",
       "      <td>9831.7</td>\n",
       "      <td>10</td>\n",
       "      <td>0.987152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259690</th>\n",
       "      <td>gvdguzxyzz</td>\n",
       "      <td>漕欣怡</td>\n",
       "      <td>100</td>\n",
       "      <td>959000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>850</td>\n",
       "      <td>9881.2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.986777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162800</th>\n",
       "      <td>BWJMPYNRR</td>\n",
       "      <td>陆明</td>\n",
       "      <td>100</td>\n",
       "      <td>982000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>850</td>\n",
       "      <td>9594.1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.985342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547561</th>\n",
       "      <td>yysnxfzutz</td>\n",
       "      <td>易梓涵</td>\n",
       "      <td>100</td>\n",
       "      <td>985000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>846</td>\n",
       "      <td>9544.6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.983035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193264</th>\n",
       "      <td>FNDRNBE</td>\n",
       "      <td>贾洋</td>\n",
       "      <td>96</td>\n",
       "      <td>975000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>823</td>\n",
       "      <td>9930.7</td>\n",
       "      <td>10</td>\n",
       "      <td>0.979743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198511</th>\n",
       "      <td>LZGXHMMXZD</td>\n",
       "      <td>文勇</td>\n",
       "      <td>99</td>\n",
       "      <td>966000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>838</td>\n",
       "      <td>9732.7</td>\n",
       "      <td>10</td>\n",
       "      <td>0.979356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364934</th>\n",
       "      <td>tlmjhlifao</td>\n",
       "      <td>乔欣怡</td>\n",
       "      <td>100</td>\n",
       "      <td>955000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>844</td>\n",
       "      <td>9653.5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.977352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-20T03:53:45.426364Z",
     "start_time": "2025-04-20T03:53:45.386485Z"
    }
   },
   "cell_type": "code",
   "source": [
    "young_high_credit_low_active = data[\n",
    "    (data['age'] <= 30) &\n",
    "    (data['income'] >= data['income'].quantile(0.75)) &\n",
    "    (data['items'] <= data['items'].quantile(0.25))\n",
    "]\n",
    "young_high_credit_low_active.head(10)"
   ],
   "id": "b54a95d83d6a3154",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      user_name chinese_name  age     income gender  credit_score    price  \\\n",
       "184     hqzurif          蔡梓萱   20   869000.0      男           318   401.05   \n",
       "261     foayoag          贾欣怡   27   962000.0      男           361   554.50   \n",
       "291      gutjda           冯浩   19   971000.0      女           313   309.97   \n",
       "307      uewfxs          董梓涵   23  1000000.0      男           337   417.98   \n",
       "368  cfouskgwhm           贺思   28   898000.0      男           370  2352.54   \n",
       "555   auoewhyfk           白欣   19   933000.0      女           311   510.94   \n",
       "559       rylew          徐梓萱   30   878000.0      男           383  2257.50   \n",
       "675     rybfwyy          曾梓萱   19   873000.0      男           306  1690.23   \n",
       "749  vbgewarfbb           文思   22   892000.0      女           332  1007.13   \n",
       "825    sgyrmedc           谭涵   18   795000.0      男           304   198.20   \n",
       "\n",
       "     items  \n",
       "184      1  \n",
       "261      1  \n",
       "291      1  \n",
       "307      2  \n",
       "368      3  \n",
       "555      1  \n",
       "559      3  \n",
       "675      3  \n",
       "749      3  \n",
       "825      2  "
      ],
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>user_name</th>\n",
       "      <th>chinese_name</th>\n",
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       "      <th>income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>hqzurif</td>\n",
       "      <td>蔡梓萱</td>\n",
       "      <td>20</td>\n",
       "      <td>869000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>318</td>\n",
       "      <td>401.05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>foayoag</td>\n",
       "      <td>贾欣怡</td>\n",
       "      <td>27</td>\n",
       "      <td>962000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>361</td>\n",
       "      <td>554.50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>gutjda</td>\n",
       "      <td>冯浩</td>\n",
       "      <td>19</td>\n",
       "      <td>971000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>313</td>\n",
       "      <td>309.97</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>uewfxs</td>\n",
       "      <td>董梓涵</td>\n",
       "      <td>23</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>337</td>\n",
       "      <td>417.98</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>cfouskgwhm</td>\n",
       "      <td>贺思</td>\n",
       "      <td>28</td>\n",
       "      <td>898000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>370</td>\n",
       "      <td>2352.54</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>auoewhyfk</td>\n",
       "      <td>白欣</td>\n",
       "      <td>19</td>\n",
       "      <td>933000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>311</td>\n",
       "      <td>510.94</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>rylew</td>\n",
       "      <td>徐梓萱</td>\n",
       "      <td>30</td>\n",
       "      <td>878000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>383</td>\n",
       "      <td>2257.50</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>rybfwyy</td>\n",
       "      <td>曾梓萱</td>\n",
       "      <td>19</td>\n",
       "      <td>873000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>306</td>\n",
       "      <td>1690.23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>vbgewarfbb</td>\n",
       "      <td>文思</td>\n",
       "      <td>22</td>\n",
       "      <td>892000.0</td>\n",
       "      <td>女</td>\n",
       "      <td>332</td>\n",
       "      <td>1007.13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>sgyrmedc</td>\n",
       "      <td>谭涵</td>\n",
       "      <td>18</td>\n",
       "      <td>795000.0</td>\n",
       "      <td>男</td>\n",
       "      <td>304</td>\n",
       "      <td>198.20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  }
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